CN102930155B - Obtain the method and device of the early-warning parameters of electricity needs - Google Patents

Abstract

The invention discloses the method and device of a kind of early-warning parameters obtaining electricity needs.Wherein, the method includes: obtain the data sequence for generating warning index；Screening acquisition data sequence is carried out according to sequence according to adjusting parameter logistic；Calculate the Trend index of the data sequence including trend term and periodic term, and obtain warning index sequence；Extract the reference index in warning index sequence and selected index；Carry out correlation calculations according to step-out time analysis model and/or K L information computation, to obtain the relative coefficient being chosen between index and reference index, and according to relative coefficient, selected index is screened, to obtain leading indicators and coincidence indicator；Leading indicators and coincidence indicator are synthesized, to obtain the leading composite index number as early-warning parameters and coincident composite Index.By means of the invention it is possible to realize accurately obtaining the early-warning parameters of electricity needs, thus formulate the effect of the counter-measure of reasonable science accurately according to short-term cyclic fluctuation.

Description

Obtain the method and device of the early-warning parameters of electricity needs

Technical field

The present invention relates to power domain, in particular to method and the dress of a kind of early-warning parameters obtaining electricity needs Put.

Background technology

Economic cyclic swing is the phenomenon of objective reality in socio-economic development, is not with people in process of economic growth Will is the objective law of transfer, and it is unpractical for attempting to force to eliminate cyclic swing by various artificial means, even at certain Under the conditions of Xie, artificial pressure elimination cyclic swing also can aggravate fluctuation.By the research to economic cycle moving law, can be Hold the fluctuation pattern of economic cycle, thus take suitable means to reduce the amplitude of business cycle fluctuation, extend economic fluctuation Cycle, thus realize the purpose of sustained economic growth.

As a key problem of macroeconomic research, the research of economic cycle is constantly subjected to national governments and numerous The attention of economist.Along with development and the progress of Econometric technology of economic theory, lot of domestic and foreign scholar starts to use Economic cycle is monitored and predicts by quantitative method, it is therefore an objective to hold each phase duration of cycle the most exactly Concrete time that length, turning point occur and expansion and the dynamics etc. of contraction, thus be that government and enterprise are for different cycles Feature and formation mechenism formulate scientific and rational counter-measure, slow down the amplitude of cyclic swing, reduce cyclic swing and send out economy The destructiveness that exhibition causes.

Economic activity is the motive force of electricity needs, therefore electricity needs also there will be cycle standing wave move, but electric power need Asking field, analyzing the means of power demand cycle fluctuation is to contrast economic growth curve for many years and power consumption growth curve, The integrated economics scholar division to China's stage of economic development, the most subjectively judges that the period of waves of electricity needs is 9-11 Year, the method the most not studying the fluctuation of short term power demand cycle.

In order to solve the problems referred to above, can directly predict the electricity needs growth rate in future by Predicting Technique, analyze it Fluctuation situation.But, it was predicted that there is a lot of defect in technology:

1, the basic data used without seasonal adjustment, in the Spring Festival, the moon two-day weekend natural law, natural law festivals or holidays, the leap year all Having a significant impact basic data, the fluctuation tendency predicted based on this will be distortion；

2, no matter use the causality models such as recurrence, per capita household electricity consumption, neutral net, or use ARIMA, logic this The time series models such as the base of a fruit, are all that the historical trend according to statistical data does reasonably extrapolation, are equivalent to pay close attention to length in Fig. 1 The Changing Pattern of phase trend, can not promptly make answering of the most compound current economic according to the Changing Pattern of long-term trend To measure.

Short-term cyclic fluctuation distortion, nothing is obtained currently for correlation technique uses Predicting Technique in electricity needs field Method acquires the early-warning parameters meeting electricity needs, thus causes formulating the rational measure tackling cyclic fluctuation Problem, the most not yet proposes effective solution.

Summary of the invention

Short-term cyclic fluctuation distortion is obtained, it is impossible to obtain for correlation technique uses Predicting Technique in electricity needs field Obtain to the early-warning parameters meeting electricity needs, thus cause formulating asking of the rational measure tackling cyclic fluctuation Topic, the most not yet proposes effective solution, provides a kind of electricity needs that obtains to this end, present invention is primarily targeted at The method and device of early-warning parameters, to solve the problems referred to above.

To achieve these goals, according to an aspect of the invention, it is provided a kind of early warning obtaining electricity needs is joined The method of number, the method includes: obtain the data sequence for generating warning index；Carry out according to sequence according to adjusting parameter logistic Screening, to obtain the data sequence including trend term and periodic term；Calculate the data sequence including trend term and periodic term Trend index, and according to Trend index, the data sequence including trend term and periodic term is filtered, to obtain early warning Index series, warning index sequence is that in data sequence, Trend index is the data increased；Extract sending out in warning index sequence Index on the basis of electricity, and extract the index in addition to generated energy for being chosen index；According to step-out time analysis model and/or K-L Information computation carries out correlation calculations to warning index, to obtain the dependency between each selected index and reference index Coefficient, and according to relative coefficient, selected index is screened, to obtain leading indicators and coincidence indicator；Refer to according to synthesis Leading indicators and coincidence indicator are synthesized by digital-to-analogue type, to obtain the leading composite index number as early-warning parameters and consistent synthesis Index.

Further, utilize step-out time analysis model and/or K-L information computation that warning index is carried out correlation calculations, With the relative coefficient between each selected index and reference index, and according to relative coefficient, selected index is sieved Choosing, includes obtaining the step of leading indicators and coincidence indicator: obtain each selected index according to equation below and benchmark refers to Relative coefficient between mark_rL:

$r_l=\frac{\underset{t=1}{\overset{n_l}{\mathrm{\Σ}}}(x_{t+l}-\stackrel{\‾}{x})(y_t-\stackrel{\‾}{y})}{\sqrt{\underset{t=1}{\overset{n_l}{\mathrm{\Σ}}}{(x_{t+l}-\stackrel{\‾}{x})}^2{(y_t-\stackrel{\‾}{y})}^2}},$

Wherein, l=0, ± 1, ± 2 ..., ± 12, Y=(y₁,y₂,…,y_nIndex on the basis of), X=(x₁,x₂,…,x_n) it is quilt Selective goal, l is the time difference, n_lFor the number of all indexs, t=1,2 ..., n is moon number；By time difference value in the first value In the range of and relative coefficient r_lMore than the selected index of first threshold as leading indicators, and time difference value is taken second In the range of value and relative coefficient r_lMore than the selected index of Second Threshold as coincidence indicator.

Further, according to step-out time analysis model and/or K-L information computation, warning index is carried out correlation calculations, With the relative coefficient between each selected index and reference index, and according to relative coefficient, selected index is sieved Choosing, includes obtaining the step of leading indicators and coincidence indicator: obtain each selected index according to equation below and benchmark refers to Relative coefficient r between mark_l:

Wherein, l=0, ± 1, ± 2 ..., ± 12, Y=(y₁,y₂,…,y_nOn the basis of) Index, X=(x₁,x₂,…,x_n) for being chosen index, l is the time difference, n_lFor the number of all indexs, t=1,2 ..., n is month Number；By time difference value in the first span and relative coefficient r_lMore than first threshold selected index as in advance refer to Target Raw performance, and by time difference value in the second span and relative coefficient r_lSelected finger more than Second Threshold It is denoted as the Raw performance for coincidence indicator；To reference index, the Raw performance of leading indicators and the Raw performance of coincidence indicator It is standardized processing, to obtain standard basis index series p_t, standard be chosen sequence q of index_t, wherein, standard is chosen Index includes standard leading indicators and standard coincidence indicator；Obtain each standard as follows and be chosen index and standard base K-L quantity of information between quasi-index_k: k_l=∑p_tln(p_t/q_t+1), wherein, l=0, ± 1 ..., ± 12, T=1,2 ..., n is moon number, and l is the time difference, n_lNumber for all indexs；By time difference value in the 3rd value In the range of and K-L quantity of information k_lIt is chosen index as leading indicators less than the standard of the 3rd threshold value, and by time difference value In four spans and K-L quantity of information k_lLess than the selected index of the 4th threshold value as coincidence indicator.

Further, according to composite index number model, leading indicators and coincidence indicator are synthesized, to obtain as early warning The leading composite index number of parameter and the step of coincident composite Index include: leading indicators and coincidence indicator carry out symmetrical change respectively Change processes, to obtain leading indicators symmetry rate of change C_{W, i}(t) and coincidence indicator symmetry rate of change C_{Z, i}T (), wherein, by as follows Formula carries out symmetrical change process to leading indicators, to obtain leading indicators symmetry rate of change C_{W, i}(t):

Wherein,Be i-th (i=1,2 ..., k_w) individual leading indicators, t=2, 3 ..., n, k_wNumber for leading indicators；By equation below, coincidence indicator is carried out symmetrical change process, unanimously refer to obtain The symmetrical rate of change C of mark_z,i(t):Wherein,Be i-th (i=1,2 ..., k_z) individual unanimously Index, t=2,3 ..., n is moon number, k_zIt it is the number of coincidence indicator；To leading indicators symmetry rate of change C_w,iT () is with consistent Index symmetry rate of change C_z,iT result that () is standardized processing and obtaining after trend adjustment carries out composite calulation, to obtain Leading composite index number and coincident composite Index.

Further, to leading indicators symmetry rate of change C_w,i(t) and coincidence indicator symmetry rate of change C_z,iT () carries out standard The result obtained after change process and trend adjustment carries out composite calulation, to obtain leading composite index number and coincident composite Index Step includes: obtain normalization factor A by equation below_{W, i}And A_{Z, i}: t=2, 3,…,n；Use normalization factor A_w,iAnd A_{Z, i}Respectively by leading indicators symmetry rate of change C_w,iT () and coincidence indicator symmetry change Rate C_z,iT () is standardized processing, to obtain standardization rate of change S_w,i(t) and S_z,i(t), wherein, T=2,3 ..., n；To standardization rate of change S_w,i(t) and S_z,i(t) be averaged rate of change process, to obtain Take standardization average rate of change V of leading indicators_wStandardization average rate of change V of (t) and coincidence indicator_z(t)；According to referring in advance Target standardization average rate of change V_wStandardization average rate of change V of (t) and coincidence indicator_zT () carries out composite calulation, to obtain Leading composite index number I_w(t) and coincident composite Index I_z(t), wherein, $I_z\left(t\right)=I_z(t-1)\×\frac{200+V_z\left(t\right)}{200-V_z\left(t\right)},$ And I_w(1)=100, I_z(1)=100。

Further, to standardization rate of change S_w,i(t) and S_z,i(t) be averaged rate of change process, with obtain in advance finger Target standardization average rate of change V_wStandardization average rate of change V of (t) and coincidence indicator_zT the step of () including: by as follows Formula is respectively by the standardization rate of change S of leading indicators_w,iThe standardization rate of change S of (t) and coincidence indicator_z,iT () is averaged Rate of change processes, to obtain average rate of change R of leading indicators_wAverage rate of change R of (t) and coincidence indicator_z(t): Wherein, λ_w,iAnd λ_z,iIt is that the i-th of leading indicators and coincidence indicator refers to respectively Target weight；Criterion factor F is obtained by equation below_w: $F_w=\left[\underset{t=2}{\overset{n}{\mathrm{\Σ}}}\right|R_w\left(t\right)|/(n-1)]/\left[\underset{t=2}{\overset{n}{\mathrm{\Σ}}}\right|R_z\left(t\right)|/(n-1)];$ According to criterion factor F_wIt is standardized the average rate of change to process, to obtain the standardization average rate of change of leading indicators V_wStandardization average rate of change V of (t) and coincidence indicator_z(t), wherein, V_w(t)=R_w(t)/F_w, V_z(t)=R_z(t)。

Further, after obtaining the data sequence for generating warning index, method also includes: in data sequence Data carry out pretreatment, pretreatment includes: fill up missing data process, revise noise data process, data smoothing process with And data normalization processes.

To achieve these goals, according to an aspect of the invention, it is provided a kind of early warning obtaining electricity needs is joined The device of number, this device includes: the first acquisition module, for obtaining the data sequence for generating warning index；First processes Module, for screening according to sequence according to adjustment parameter logistic, to obtain the data sequence including trend term and periodic term； First computing module, for calculating the Trend index of the data sequence including trend term and periodic term, and according to Trend index Filtering the data sequence including trend term and periodic term, to obtain warning index sequence, warning index sequence is number It is the data increased according to Trend index in sequence；First extraction module, is base for extracting the generated energy in warning index sequence Quasi-index, and extract the index in addition to generated energy for being chosen index；Second computing module, for according to step-out time analysis model And/or K-L information computation carries out correlation calculations to warning index, to obtain between each selected index and reference index Relative coefficient, and according to relative coefficient, selected index is screened, to obtain leading indicators and coincidence indicator；The Two processing modules, for synthesizing leading indicators and coincidence indicator according to composite index number model, to obtain as early warning ginseng The leading composite index number of number and coincident composite Index.

Further, the second computing module includes: the first sub-computing module, for obtaining each selected according to equation below Select the relative coefficient r between index and reference index_l:Wherein, l=0, ± 1, ± 2 ..., ± 12, Y=(y₁,y₂,…,y_nIndex on the basis of), X=(x₁,x₂,…,x_n) for being chosen index, l is the time difference, n_lFor institute There is a number of index, t=1,2 ..., n is moon number；First sub-processing module, is used for time difference value in the first span In and relative coefficient r_lMore than the selected index of first threshold as leading indicators, and by time difference value at the second value model Enclose interior and relative coefficient r_lMore than the selected index of Second Threshold as coincidence indicator.

Further, the second computing module includes: the second sub-computing module, for obtaining each selected according to equation below Select the relative coefficient r between index and reference index_l:Wherein, l=0, ± 1, ± 2 ..., ± 12, Y=(y₁,y₂,…,y_nIndex on the basis of), X=(x₁,x₂,…,x_n) for being chosen index, l is the time difference, n_lFor institute There is a number of index, t=1,2 ..., n is moon number；Second sub-processing module, is used for time difference value in the first span In and relative coefficient r_lMore than the selected index of first threshold as the Raw performance of leading indicators, and time difference value is existed In second span and relative coefficient r_lMore than the selected index of Second Threshold as the Raw performance of coincidence indicator；The Three sub-processing modules, for carrying out standard to the Raw performance of reference index, the Raw performance of leading indicators and coincidence indicator Change processes, to obtain standard basis index series p_t, standard be chosen sequence q of index_t, wherein, standard is chosen index and includes Standard leading indicators and standard coincidence indicator；3rd sub-computing module, is chosen for obtaining each standard as follows K-L quantity of information between index and standard basis index_kL:k_l=∑p_tln(p_t/q_t+1), wherein, l=0, ± 1 ..., ± 12, T=1,2 ..., n is moon number, and l is the time difference, n_lNumber for all indexs；

4th sub-processing module, for by time difference value in the 3rd span and K-L quantity of information k_lLess than the 3rd threshold The standard of value is chosen index as leading indicators, and by time difference value in the 4th span and K-L quantity of information k_lIt is less than The selected index of the 4th threshold value is as coincidence indicator.

Further, the second processing module includes: the 5th sub-processing module, for leading indicators and coincidence indicator difference Carry out symmetrical change process, to obtain leading indicators symmetry rate of change C_{W, i}(t) and coincidence indicator symmetry rate of change C_{Z, i}(t), the Five sub-processing modules include: the 4th sub-computing module, for leading indicators being carried out symmetrical change process by equation below, with Obtain leading indicators symmetry rate of change C_w,i(t):Wherein,Be i-th (i=1, 2 ..., k_w) individual leading indicators, t=2,3 ..., n is moon number, k_wNumber for leading indicators；5th sub-computing module, is used for leading to Cross equation below and coincidence indicator is carried out symmetrical change process, to obtain coincidence indicator symmetry rate of change C_z,i(t):Wherein,Be i-th (i=1,2 ..., k_z) individual coincidence indicator, t=2,3 ..., n, k_zIt is The number of coincidence indicator；6th sub-processing module, for leading indicators symmetry rate of change C_w,iT () and coincidence indicator symmetry become Rate C_z,iT result that () is standardized processing and obtaining after trend adjustment carries out composite calulation, refers to obtain to synthesize in advance Number and coincident composite Index.

Further, the 6th sub-processing module includes: the 6th sub-computing module, for obtaining standardization by equation below Factors A_w,iAnd A_{Z, i}: t=2,3,…,n；7th sub-processing module, is used for using mark Standardization factors A_w,iAnd A_{Z, i}Respectively by leading indicators symmetry rate of change C_w,i(t) and coincidence indicator symmetry rate of change C_z,i(t) carry out Standardization, to obtain standardization rate of change S_{W, i}(t) and S_z,i(t), wherein, t= 2,3,…,n；

8th sub-processing module, for standardization rate of change S_w,i(t) and S_z,i(t) be averaged rate of change process, with Obtain standardization average rate of change V of leading indicators_wStandardization average rate of change V of (t) and coincidence indicator_z(t)；7th son meter Calculate module, for standardization average rate of change V according to leading indicators_wStandardization average rate of change V of (t) and coincidence indicator_z T () carries out composite calulation, to obtain leading composite index number I_w(t) and coincident composite Index I_z(t), wherein, $I_z\left(t\right)=I_z(t-1)\×\frac{200+V_z\left(t\right)}{200-V_z\left(t\right)},$ And I_w(1)=100, I_z(1)=100。

Further, the 8th sub-processing module includes: the 9th sub-processing module, and being used for respectively will be in advance by equation below The standardization rate of change S of index_w,iThe standardization rate of change S of (t) and coincidence indicator_z,i(t) be averaged rate of change process, to obtain Take average rate of change R of leading indicators_wAverage rate of change R of (t) and coincidence indicator_z(t):

$R_w\left(t\right)=\frac{\underset{i=1}{\overset{k_w}{\mathrm{\Σ}}}{S}_{w,i}\left(t\right){\mathrm{\λ}}_{w,i}}{\underset{i=1}{\overset{k_w}{\mathrm{\Σ}}}{\mathrm{\λ}}_{w,i}},$ $R_z\left(t\right)=\frac{\underset{i=1}{\overset{k_z}{\mathrm{\Σ}}}{S}_{z,i}\left(t\right){\mathrm{\λ}}_{z,i}}{\underset{i=1}{\overset{k_Z}{\mathrm{\Σ}}}{\mathrm{\λ}}_{z,i}},$ Wherein, λ_w,iAnd λ_z,iIt is that leading indicators refers to consistent respectively The weight of target i-th index；8th sub-computing module, for obtaining criterion factor F by equation below_w: $F_w=\left[\underset{t=2}{\overset{n}{\mathrm{\Σ}}}\right|R_w\left(t\right)|/(n-1)]/\left[\underset{t=2}{\overset{n}{\mathrm{\Σ}}}\right|R_z\left(t\right)|/(n-1)];$ 9th sub-computing module, for according to criterion factor F_wEnter The column criterion average rate of change processes, to obtain standardization average rate of change V of leading indicators_wThe standard of (t) and coincidence indicator Change average rate of change V_z(t), wherein, V_w(t)=R_w(t)/F_w, V_z(t)=R_z(t)。

Further, after performing acquisition module, device also includes: the tenth sub-processing module, for data sequence In data carry out pretreatment, pretreatment includes: fill up missing data process, revise noise data process, data smoothing process And data normalization processes.

By the method and device of the early-warning parameters of the acquisition electricity needs of the application, in obtaining original data sequence After trend term and periodic term, by data sequence screening and calculating are obtained leading indicators and coincidence indicator, then by above-mentioned Leading indicators and coincidence indicator index synthetic model synthesis obtain early-warning parameters, and analyze electricity needs week according to early-warning parameters Phase property fluctuates, and solves and uses in electricity needs field Predicting Technique to obtain short-term cyclic fluctuation distortion, nothing in prior art Method acquires the early-warning parameters meeting electricity needs, thus causes to formulate reasonably reply according to power cycle fluctuation The measure of cyclic fluctuation, it is achieved that accurately obtain the early-warning parameters of electricity needs, thus accurately according to short-term periodic wave The effect of the dynamic counter-measure formulating reasonable science, and then slow down the amplitude of cyclic swing, reduce cyclic swing to electric power row The destructiveness that industry and economic development cause.

Accompanying drawing explanation

Accompanying drawing described herein is used for providing a further understanding of the present invention, constitutes the part of the application, this Bright schematic description and description is used for explaining the present invention, is not intended that inappropriate limitation of the present invention.In the accompanying drawings:

Fig. 1 is according to the schematic diagram of the cyclic fluctuation of electricity needs in prior art；

Fig. 2 is the structural representation of the device of the early-warning parameters of the acquisition electricity needs according to the present invention；

Fig. 3 is the flow chart of the method for the early-warning parameters of acquisition electricity needs according to embodiments of the present invention；

Fig. 4 is the detail flowchart of the method for the early-warning parameters of acquisition electricity needs according to embodiments of the present invention；

Fig. 5 is the method schematic diagram of the acquisition coincidence indicator according to embodiment illustrated in fig. 4 and leading indicators；And

Fig. 6 is the trend adjustment schematic diagram according to embodiment illustrated in fig. 4.

Detailed description of the invention

It should be noted that in the case of not conflicting, the embodiment in the application and the feature in embodiment can phases Combination mutually.Describe the present invention below with reference to the accompanying drawings and in conjunction with the embodiments in detail.

Fig. 2 is the structural representation of the device of the early-warning parameters of the acquisition electricity needs according to the present invention.As in figure 2 it is shown, This device includes: acquisition module 10, for obtaining the data sequence for generating warning index；First processing module 30, is used for Screen according to sequence according to adjusting parameter logistic, to obtain the data sequence including trend term and periodic term；First calculates Module 50, for calculating the Trend index of the data sequence including trend term and periodic term, and according to Trend index to comprising The data sequence having trend term and periodic term filters, and to obtain warning index sequence, warning index sequence is data sequence Middle Trend index is the data increased；First extraction module 70, refers on the basis of extracting the generated energy in warning index sequence Mark, and extract the index in addition to power consumption for being chosen index；Second computing module 90, for according to step-out time analysis model And/or K-L information computation carries out correlation calculations to warning index, to obtain between each selected index and reference index Relative coefficient, and according to relative coefficient, selected index is screened, to obtain leading indicators and coincidence indicator；The Two processing modules 110, for synthesizing leading indicators and coincidence indicator according to composite index number model, to obtain as early warning The leading composite index number of parameter and coincident composite Index.

Use the device of early-warning parameters of the acquisition electricity needs of the present invention, be used for acquisition generating by acquisition module pre- The data sequence of alert index, then data sequence is screened by the first processing module, and acquisition includes trend term and cycle The data sequence of item, the first computing module calculates the Trend index of above-mentioned data sequence afterwards, and according to Trend index to above-mentioned Data sequence filters, and to obtain in data sequence Trend index for the warning index sequence increased, then carries by first Delivery block extracts index on the basis of the generated energy in warning index sequence, and extracts the index in addition to power consumption for being chosen to refer to Mark, the second computing module carries out dependency meter according to step-out time analysis model and/or K-L information computation to warning index afterwards Calculate, to obtain the relative coefficient between each selected index and reference index, and according to relative coefficient to selected finger Mark screens, and to obtain leading indicators and coincidence indicator, the second last processing module will refer in advance according to composite index number model Mark and coincidence indicator synthesize, to obtain the leading composite index number as early-warning parameters and coincident composite Index.By this Shen The device of the early-warning parameters of acquisition electricity needs please, first obtains the trend term in data sequence and periodic term, then to data Sequence carries out processing acquisition leading indicators and coincidence indicator, and above-mentioned leading indicators and coincidence indicator index synthetic model is closed Become to obtain early-warning parameters, solve and prior art uses in electricity needs field Predicting Technique obtain short-term cyclic fluctuation mistake Very, it is impossible to acquire the early-warning parameters meeting electricity needs, thus cause to formulate rationally according to power cycle fluctuation The problem of counter-measure, it is achieved that accurately obtain the early-warning parameters of electricity needs, thus accurately according to short-term periodic wave The dynamic effect formulating rational counter-measure, and then slow down the amplitude of cyclic swing, reduce cyclic swing to power industry and The destructiveness that economic development causes.

Specifically, in electricity needs field, the data sequence for generating warning index includes 4 parts: long-term trend item, Circulation item that industry expansion-flourishing contractions-decline-further expansion quasi-periodic rule causes, spring, summer, autumn and winter or monthly natural law difference The random entry that the item in season that causes, ignorance factor cause.By the device of the early-warning parameters of the acquisition electricity needs of the present invention, First reject the item and random entry in season in original data sequence, obtain the data sequence of trend term therein and periodic term, so After analyze or K-L quantity of information skill using season GDP or monthly industrial added value as reference index, used time difference correlation Art screens several leading indicators and coincidence indicator from other economic indicators substantial amounts of, finally uses index synthetic model respectively will Several leading indicators synthesize beforehand index, several coincidence indicators are synthesized same index, gets early-warning parameters, just Can realize utilizing the purpose of the future trend of the advanced judgement coincidence indicator of beforehand index according to early-warning parameters.

In the above embodiment of the present invention, data sequence is screened, to initial data by the first processing module 30 Sequence does seasonal adjustment, i.e. seasonally adjusts and the impact of random factor, with long-term trend item (i.e. in above-described embodiment Trend term) and short-term circulation item (i.e. periodic term in above-described embodiment) based on, synthesized by index screening and index, use scape The fluctuation tendency of gas early warning technology research electricity needs, and, above-described embodiment economic alarming electricity needs, from substantial amounts of warp Ji index screens leading indicators and coincidence indicator, is used for judging the cyclic fluctuation of electricity needs so that judged result is more Accurately so that for formulating the cyclic fluctuation of reasonable counter-measure more accurately rationally.

In the above-mentioned practical work example of the application, the second computing module 90 includes: the first sub-computing module, for according to as follows Formula obtains the relative coefficient r between each selected index and reference index_l:

Wherein, l=0, ± 1, ± 2 ..., ± 12, Y=(y₁,y₂,…,y_nOn the basis of) Index, X=(x₁,x₂,…,x_n) for being chosen index, l is the time difference, n_lNumber for all indexs；First sub-processing module, uses In by time difference value in the first span and relative coefficient r_lMore than first threshold selected index as in advance refer to Mark, and by time difference value in the second span and relative coefficient r_lMore than the selected index of Second Threshold as unanimously Index.

Specifically, model Preliminary screening coincidence indicator is analyzed with reference index as " mark post " screened, used time difference correlation And leading indicators.The all selected index in addition to the reference index time difference all in advance or in lag period l(i.e. above-described embodiment) (l=0, ± 1, ± 2 ..., ± 12), the first sub-computing module calculates each selected index and reference index respectively according to following formula Relative coefficient r_l:

$r_l=\frac{\underset{t=1}{\overset{n_l}{\mathrm{\Σ}}}(x_{t+l}-\stackrel{\‾}{x})(y_t-\stackrel{\‾}{y})}{\sqrt{\underset{t=1}{\overset{n_l}{\mathrm{\Σ}}}{(x_{t+l}-\stackrel{\‾}{x})}^2{(y_t-\stackrel{\‾}{y})}^2}},$ L=0, ± 1, ± 2 ..., ± 12,

Y=(y in above formula₁,y₂,…,y_nIndex on the basis of), X=(x₁,x₂,…,x_n) for being chosen index, r is phase relation Number, l represents the advanced or lag period (i.e. the time difference), and l represents advanced when taking negative, represents delayed when taking positive number, l be referred to as the time difference or Postpone number,WithIt is respectively sequence X and the meansigma methods of Y.n_lIt it is the data amount check of all indexs.Then maximum time difference correlation coefficient Being considered the time difference dependency relation reflecting selected index with reference index, the corresponding number l that postpones represents the advanced or lag period, i.e. Make time difference relative coefficient r_lMaximum delay number l is exactly this selected index and the advanced of reference index or lag period.

According to above-described embodiment of the application, the second computing module 90 can also include: the second sub-computing module, for root The relative coefficient r between each selected index and reference index is obtained according to equation below_l:

Wherein, l=0, ± 1, ± 2 ..., ± 12, Y=(y₁,y₂,…,y_nOn the basis of) Index, X=(x₁,x₂,…,x_n) for being chosen index, l is the time difference, n_lNumber for all indexs；Second sub-processing module, uses In by time difference value in the first span and relative coefficient r_lMore than first threshold selected index as in advance refer to Target Raw performance, and by time difference value in the second span and relative coefficient r_lSelected finger more than Second Threshold It is denoted as the Raw performance for coincidence indicator；3rd sub-processing module, for reference index, the Raw performance of leading indicators and The Raw performance of coincidence indicator is standardized processing, to obtain standard basis index series p_t, standard be chosen the sequence of index Row q_t, wherein, standard is chosen index and includes standard leading indicators and standard coincidence indicator；3rd sub-computing module, is used for Obtain the K-L quantity of information k that each standard is chosen between index and standard basis index as follows_l:

k_l=∑p_tln(p_t/q_t+1), wherein, l=0, ± 1 ..., ± 12, T= 1,2 ..., n, l are the time difference, n_lNumber for all indexs；4th sub-processing module, is used for time difference value at the 3rd value model Enclose interior and K-L quantity of information k_lIt is chosen index as leading indicators less than the standard of the 3rd threshold value, and by time difference value the 4th In span and K-L quantity of information k_lLess than the selected index of the 4th threshold value as coincidence indicator.Wherein, the first span Can be less than-3, the second span may be greater than or equal to-2 and less than or equal to 2, first threshold can be 0.7, the Two threshold values can also be 0.7.Wherein, t is moon number.

Specifically, relative coefficient r is got by the second sub-computing module calculating_lAfterwards, the second sub-processing module will Time difference value is in the first span and relative coefficient r_lMore than the selected index of first threshold as leading indicators Raw performance, and by time difference value in the second span and relative coefficient r_lMake more than the selected index of Second Threshold For the Raw performance of coincidence indicator, reference index is done standardization by the 3rd sub-processing module afterwards, the standard base after process Quasi-index series is designated as p_t:

$p_t=y_t/\underset{t=1}{\overset{n}{\mathrm{\Σ}}}{y}_t,$ T=1,2 ..., n,

Leading indicators and the coincidence indicator of primary election are also done standardization by the 3rd sub-processing module, the standard quilt after process The sequence of selective goal is designated as q_t:

$q_t=x_t/\underset{t=1}{\overset{n}{\mathrm{\Σ}}}{x}_t,$ T=1,2 ..., n,

Then, about the K-L information of reference index after the 4th sub-processing module is calculated as follows each primary election beacon delay l Amount k_l:

k_l=∑p_tln(p_t/q_t+1), l=0, ± 1 ..., ± 12

Wherein, l represents the advanced or lag period, and l represents advanced when taking negative, represents delayed, when l is referred to as when taking positive number Difference, n_lBeing the data amount check (numbers of the most all indexs) after data are evened up, the t in above-mentioned formula represents month, and j represents year.

More specifically, after calculating 2L+1 K-L quantity of information, from k_lValue is selected minima k_l′As selected finger Mark x is about the K-L quantity of information of reference index y, i.e.Its corresponding delay number l^*It is exactly that selected index is optimal Advanced or delayed moon number (season).K-L quantity of information is closer to 0, illustrate index x and reference index y closer to.And will filter out Leading indicators and the coincidence indicator come are designated as W respectively⁽³⁾And Z⁽³⁾。

According to above-described embodiment of the application, the second processing module 110 may include that the 5th sub-processing module, for right Leading indicators and coincidence indicator carry out symmetrical change process respectively, to obtain leading indicators symmetry rate of change C_w,iT () refers to consistent The symmetrical rate of change C of mark_{Z, i}T (), the 5th sub-computing module includes: the 4th sub-computing module, is used for by equation below referring in advance Mark carries out symmetrical change process, to obtain leading indicators symmetry rate of change C_w,i(t):

Wherein,Be i-th (i=1,2 ..., k_w) individual leading indicators, t=2, 3 ..., n, k_wThe number of leading indicators；And the 5th sub-computing module, for coincidence indicator being carried out symmetry by equation below Change process, to obtain coincidence indicator symmetry rate of change C_{Z, i}(t):

Wherein,Be i-th (i=1,2 ..., k_z) individual coincidence indicator, t=2, 3 ..., n, k_zIt it is the number of coincidence indicator；6th sub-processing module, for leading indicators symmetry rate of change C_w,iT () is with consistent Index symmetry rate of change C_z,iT result that () is standardized processing and obtaining after trend adjustment carries out composite calulation, to obtain Leading composite index number and coincident composite Index.

Specifically, the 5th sub-processing module index synthetic model is sought the symmetrical rate of change of index and is processed by the 6th son Module is by its standardization, by the 4th sub-computing module in the 5th sub-processing module according to following formula pairAsk symmetrical Rate of change C_w,i(t):

T=2,3 ..., n, wherein,Be i-th (i=1,2 ..., k_w) individual elder generation Row index, k_wIt it is the number of leading indicators.

By the 5th sub-computing module according to following formula pairSeek symmetrical rate of change C_z,i(t):

T=2,3 ..., n, wherein,Be i-th (i=1,2 ..., k_z) individual one Cause index, k_zIt it is the number of coincidence indicator.

In above-described embodiment of the application, the 6th sub-processing module may include that the 6th sub-computing module, is used for passing through Equation below obtains normalization factor A_{W, i}And A_{Z, i}: t=2,3,…,n；7th son Processing module, is used for using normalization factor A_{W, i}And A_{Z, i}Respectively by leading indicators symmetry rate of change C_w,i(t) and coincidence indicator pair Claim rate of change C_z,iT () is standardized processing, to obtain standardization rate of change S_w,i(t) and S_z,i(t), wherein,

$S_{w,i}\left(t\right)=\frac{C_{w,i}\left(t\right)}{A_{w,i}},$ $S_{z,i}\left(t\right)=\frac{C_{z,i}\left(t\right)}{A_{z,i}},$ t=2,3,…,n；

8th sub-processing module, for standardization rate of change S_w,i(t) and S_z,i(t) be averaged rate of change process, with Obtain standardization average rate of change V of leading indicators_wStandardization average rate of change V of (t) and coincidence indicator_z(t)；7th son meter Calculate module, for standardization average rate of change V according to leading indicators_wStandardization average rate of change V of (t) and coincidence indicator_z T () carries out composite calulation, to obtain leading composite index number I_w(t) and coincident composite Index I_z(t), wherein, $I_z\left(t\right)=I_z(t-1)\×\frac{200+V_z\left(t\right)}{200-V_z\left(t\right)},$ And I_w(1)=100, I_z(1)=100。

Specifically, the 6th sub-computing module according to equation below to leading indicators symmetry rate of change C_w,i(t) and coincidence indicator Symmetrical rate of change C_z,iT () does standardized calculation so that it is average absolute value is equal to 1, obtains normalization factor A_w,iAnd A_{Z, i}:

$A_{w,i}=\underset{t=2}{\overset{n}{\mathrm{\Σ}}}\frac{\left|C_{w,i}\left(t\right)\right|}{n-1},$ $A_{z,i}=\underset{t=2}{\overset{n}{\mathrm{\Σ}}}\frac{\left|C_{z,i}\left(t\right)\right|}{n-1},$ T=2,3 ..., n,

Then the 7th sub-processing module is according to A_w,iAnd A_{Z, i}Respectively by C_w,i(t) and C_z,iT () standardization, obtains standardization and becomes Rate S_w,i(t) and S_z,i(t):

T=2,3 ..., n, the 8th sub-processing module changes according to standardization afterwards Rate S_w,i(t) and S_z,i(t) be averaged rate of change process, to obtain standardization average rate of change V of leading indicators_w(t) and one Cause standardization average rate of change V of index_z(t), and averagely become according to the standardization of leading indicators by the 7th sub-computing module Rate V_wStandardization average rate of change V of (t) and coincidence indicator_zT () carries out composite calulation, to obtain leading composite index number I_w(t) With coincident composite Index I_z(t), specifically: make I_w(1)=100, I_z(1)=100, then

$I_w\left(t\right)=I_w(t-1)\×\frac{200+V_w\left(t\right)}{200-V_{w}t},$ $I_z\left(t\right)=I_z(t-1)\×\frac{200+V_z\left(t\right)}{200-V_z\left(t\right)}.$

More specifically, getting leading composite index number I_w(t) and coincident composite Index I_zAfter (t), electric power can be needed Asking trend to be adjusted, method is as follows:

According to following compound interest formula each sequence of coincidence indicator group obtained respectively respective average rate of increase:

$r_i=(\sqrt[m_i]{C_{\mathrm{Li}}/C_{\mathrm{Ii}}}-1)\×100,$ i=1,2,…,k_z,

Wherein, Fig. 6 is the trend adjustment schematic diagram according to embodiment illustrated in fig. 4, and as shown in Figure 6, t is month,WithIt is that coincidence indicator group i-th index circulates with last at first respectively Meansigma methods, m_IiWith m_LiBe respectively coincidence indicator group i-th index at first with the most metacyclic moon number, k₂It it is coincidence indicator Number, m_iIt is the center circulated at first to the moon number between the most metacyclic center.

Then the average rate of increase G of coincidence indicator group is obtained_r, and as target trend:Afterwards To the leading and initial composite index number I of coincidence indicator_w(t) and I_zT () obtains their respective balanced growth with compound interest formula respectively Rate r '_wWith r '_z:

$r_w^{\′}(\sqrt[m_w]{C_{\mathrm{Lw}}/C_{\mathrm{Iw}}}-1)\×100,$ $r_z^{\′}(\sqrt[m_z]{C_{\mathrm{Lz}}/C_{\mathrm{Iz}}}-1)\×100,$

Wherein,

Standardization average rate of change V to leading indicators group and coincidence indicator group again_w(t) and V_zT () does trend adjustment:

V′_w(t)=V_w(t)+(G_r-r′_w), V '_z(t)=V_z(t)+(G_r-r′_z), t=2,3 ..., n.Then according to above-mentioned enforcement Method in example calculates composite index number: make I '_w(1)=100, I '_z(1)=100, then

${I^{\′}}_w\left(t\right)={I^{\′}}_w(t-1)\×\frac{200+{V^{\′}}_w\left(t\right)}{200-{V^{\′}}_w\left(t\right)},$ ${I^{\′}}_z\left(t\right)={I^{\′}}_z(t-1)\×\frac{200+{V^{\′}}_z\left(t\right)}{200-{V^{\′}}_z\left(t\right)},$

Generate with leading composite index number CI that the benchmark time is 100_w(t) and coincident composite Index CI_z(t):

${\mathrm{CI}}_w\left(t\right)=(I_w^{\′}\left(t\right)/\stackrel{\‾}{I_w^{\′}}\×100),$ ${\mathrm{CI}}_z\left(t\right)=(I_z^{\′}\left(t\right)/\stackrel{\‾}{I_z^{\′}})\×100,$

WhereinWithIt is I ' respectively_w(t) and I '_zT () is in the meansigma methods in benchmark time.

According to above-described embodiment of the application, the 8th sub-processing module may include that the 9th sub-processing module, is used for passing through Equation below is respectively by the standardization rate of change S of leading indicators_w,iThe standardization rate of change S of (t) and coincidence indicator_z,i(t) carry out The average rate of change processes, to obtain average rate of change R of leading indicators_wAverage rate of change R of (t) and coincidence indicator_z(t):

$R_w\left(t\right)=\frac{\underset{i=1}{\overset{k_w}{\mathrm{\Σ}}}{S}_{w,i}\left(t\right){\mathrm{\λ}}_{w,i}}{\underset{i=1}{\overset{k_w}{\mathrm{\Σ}}}{\mathrm{\λ}}_{w,i}},$ $R_z\left(t\right)=\frac{\underset{i=1}{\overset{k_z}{\mathrm{\Σ}}}{S}_{z,i}\left(t\right){\mathrm{\λ}}_{z,i}}{\underset{i=1}{\overset{k_z}{\mathrm{\Σ}}}{\mathrm{\λ}}_{z,i}},$ Wherein, λ_w,iAnd λ_z,iIt is leading indicators and respectively Cause the weight of the i-th index of index；8th sub-computing module, for obtaining criterion factor F by equation below_w:

$F_w=\left[\underset{t=2}{\overset{n}{\mathrm{\Σ}}}\right|R_w\left(t\right)|/(n-1)]/\left[\underset{t=2}{\overset{n}{\mathrm{\Σ}}}\right|R_z\left(t\right)|/(n-1)];$

9th sub-computing module, for according to criterion factor F_wIt is standardized the average rate of change to process, to obtain Take standardization average rate of change V of leading indicators_wStandardization average rate of change V of (t) and coincidence indicator₂(t), wherein, V_w(t) =R_w(t)/F_w, V_z(t)=R_z(t)。

Specifically, by equation below respectively by the standardization rate of change S of leading indicators_w,iThe standard of (t) and coincidence indicator Change rate of change S_z,i(t) be averaged rate of change process, to obtain average rate of change R of leading indicators_w(t) and coincidence indicator Average rate of change R_z(t):

$R_w\left(t\right)=\frac{\underset{i=1}{\overset{k_w}{\mathrm{\Σ}}}{S}_{w,i}\left(t\right){\mathrm{\λ}}_{w,i}}{\underset{i=1}{\overset{k_w}{\mathrm{\Σ}}}{\mathrm{\λ}}_{w,i}},$ $R_z\left(t\right)=\frac{\underset{i=1}{\overset{k_z}{\mathrm{\Σ}}}{S}_{z,i}\left(t\right){\mathrm{\λ}}_{z,i}}{\underset{i=1}{\overset{k_Z}{\mathrm{\Σ}}}{\mathrm{\λ}}_{z,i}},$ λ_w,iAnd λ_z,iIt it is leading respectively and coincidence indicator group The weight of i-th index.

Then according to equation below gauge index normalization factor F_w:

$F_w=\left[\underset{t=2}{\overset{n}{\mathrm{\Σ}}}\right|R_w\left(t\right)|/(n-1)]/\left[\underset{t=2}{\overset{n}{\mathrm{\Σ}}}\right|R_z\left(t\right)|/(n-1)];$

Finally according to criterion factor F_wIt is standardized the average rate of change to process, to obtain the standard of leading indicators Change average rate of change V_wStandardization average rate of change V of (t) and coincidence indicator_z(t):

V_w(t)=R_w(t)/F_w, V_z(t)=R_z(t), t=2,3 ..., n, wherein, by the mean change of coincidence indicator sequence The amplitude of rate goes to adjust leading indicators sequence and the average rate of change of lagging indicator sequence, its purpose is to two indexes to work as Make a harmonious system to apply.

According to above-described embodiment of the application, after performing acquisition module 10, device can also include: the tenth son processes Module, for the data in data sequence are carried out pretreatment, pretreatment includes: fills up missing data and processes, revises noise number Process according to process, data smoothing and data normalization processes.

Fig. 3 is the flow chart of the method for the early-warning parameters of acquisition electricity needs according to embodiments of the present invention.Fig. 4 is basis The detail flowchart of the method for the early-warning parameters of the acquisition electricity needs of the embodiment of the present invention.

As shown in Figure 3 and Figure 4, the method comprises the steps:

Step S102, obtains the data sequence for generating warning index.

Wherein, this step can be realized by step S202 in Fig. 4: collects macroeconomy and the electricity needs moon number of degrees According to, and do pretreatment.

Step S104, screens according to sequence according to adjusting parameter logistic, includes trend term and periodic term to obtain Data sequence.Wherein, the method can being realized by step S204 in Fig. 4: by data sequence being made seasonal adjustment, obtaining institute There are trend term and the periodic term of index.

Specifically, in electricity needs field, the data sequence generating warning index includes 4 parts: long-term trend item (i.e. becomes Gesture item), industry expansion-flourishing contraction-decline-further expansion quasi-periodic rule cause circulation item (i.e. periodic term), the spring and summer autumn Data are the magnitude of value and physical quantity by the random entry that winter or item in season that monthly natural law difference causes, ignorance factor cause Index makees seasonal adjustment, rejects the item and random entry in season in data sequence, obtains the data of trend term therein and periodic term Sequence.

Step S106, calculates the Trend index of the data sequence including trend term and periodic term, and according to Trend index Filtering the data sequence including trend term and periodic term, to obtain warning index sequence, warning index sequence is number It is the data increased according to Trend index in sequence.Wherein, this step can be realized by step S206 to S208: step S206 is sentenced Whether the achievement data in disconnected data sequence is growth indices class achievement data, and where it has, perform step S208: The growth rate of parameter, in the case of the achievement data in data sequence is not growth indices class achievement data, performs Step S210.

Step S108, extracts index on the basis of the generated energy in warning index sequence, and extracts the finger in addition to generated energy It is designated as being chosen index.Wherein, the method is realized by step S210: determine reference index.

Specifically in electricity needs field, using season GDP or monthly industrial added value as reference index.

Step S110, carries out correlation calculations according to step-out time analysis model and/or K-L information computation to warning index, To obtain the relative coefficient between each selected index and reference index, and according to relative coefficient, selected index is entered Row filter, to obtain leading indicators and coincidence indicator.Wherein, step S212 can realize the method: sieves with step-out time analysis model Select initial coincidence indicator and initial leading indicators；Then step S214 is performed: by the K-L information computation coincidence indicator from primary election With leading indicators screens final coincidence indicator and leading indicators.

Specifically, can be consistent by step-out time analysis model Preliminary screening from the index such as macroeconomy, the output of industrial product Index and leading indicators.

Step S112, synthesizes leading indicators and coincidence indicator according to composite index number model, to obtain as early warning The leading composite index number of parameter and coincident composite Index.Wherein, said method is realized by execution step S216: synthesize with index Model synthesizes same index and beforehand index coincidence indicator and leading indicators respectively；Then step S218 is performed: according to one Cause index and beforehand index obtains early-warning parameters, and use early-warning parameters to analyze power demand cycle fluctuation.

Specifically, respectively several leading indicators are synthesized beforehand index with index synthetic model, several are consistent Index synthesizes same index, gets early-warning parameters, it is possible to realize utilizing the advanced of beforehand index according to early-warning parameters Judge the purpose of the future trend of coincidence indicator.

The method using the early-warning parameters of the acquisition electricity needs of the present invention, by being used for generating warning index by acquisition Data sequence screens, and obtains the data sequence including trend term and periodic term, calculates above-mentioned data sequence afterwards Trend index, and filtering this data sequence according to Trend index, to obtain Trend index in data sequence for increasing Warning index sequence, then extracts index on the basis of the generated energy in warning index sequence, and extracts the finger in addition to power consumption It is designated as being chosen index, according to step-out time analysis model and/or K-L information computation, warning index is carried out dependency meter afterwards Calculate, to obtain the relative coefficient between each selected index and reference index, and according to relative coefficient to selected finger Mark screens, to obtain leading indicators and coincidence indicator, finally according to composite index number model by leading indicators and coincidence indicator Synthesize, to obtain the leading composite index number as early-warning parameters and coincident composite Index.By the acquisition electric power of the application The method of the early-warning parameters of demand, is first obtained the trend term in data sequence and periodic term, is then obtained by screening and refer in advance Mark and coincidence indicator, and above-mentioned leading indicators and coincidence indicator index synthetic model are synthesized acquisition early-warning parameters, solve Prior art use Predicting Technique obtain short-term cyclic fluctuation distortion in electricity needs field, it is impossible to acquire and meet electricity The early-warning parameters of power demand, thus the problem causing to formulate rational counter-measure according to power cycle fluctuation, it is achieved The early-warning parameters of accurate acquisition electricity needs, thus formulate according to short-term cyclic fluctuation accurately and reasonably tackle periodically The effect of the measure of fluctuation, and then slow down the amplitude of cyclic swing, reduce cyclic swing and power industry and economic development are made The destructiveness become.

In the above embodiment of the present invention, first data sequence is screened by step S102, namely to original number Do seasonal adjustment according to sequence, i.e. seasonally adjust and the impact of random factor, with long-term trend item (i.e. in above-described embodiment Trend term) and short-term circulation item (i.e. periodic term in above-described embodiment) based on, synthesized by index screening and index, use The fluctuation tendency of prosperity early warning technical research electricity needs, and, above-described embodiment economic alarming electricity needs, from substantial amounts of Economic indicator is screened leading indicators and coincidence indicator, is used for judging the cyclic fluctuation of electricity needs so that judged result is more Add accurately so that for formulating the cyclic fluctuation of reasonable counter-measure more accurately rationally.

Wherein, the pretreatment in step S202 may include that filling up missing data processes, revises noise data process, number Process according to smoothing processing and data normalization, and the data sequence after processing is as Y⁽⁰⁾。

Specifically, in above-described embodiment of the application, before performing step S204, the method also includes walking as follows Rapid:

(1) festivals or holidays are removed or given month that other reason causes working days are how many between different year difference Not, concrete grammar is as follows:

In this embodiment, if monthly data sequence to be analyzed(t=1,2 ..., n) total m, n month, n=m × 12, t represent moon number, and i represents number of weeks, and j represents year number, and the most each moon real work natural law is D_t(t=1,2 ..., n), m puts down The work natural law in equal each month is:(L=1,2 ..., 12), work natural law can be obtained and adjust system Number Sequence p_t:

T=1,2 ..., n, then according to work natural law regulation coefficient p_tObtain the moon number of degrees after adjusting in month According to sequence Y⁽¹⁾: $Y_t^{\left(1\right)}=Y_t^{\left(0\right)}/p_t.$

(2) the inside of a week in sequence is adjusted: from former sequence, extract the inside of a week (week structure) because of each moon out no The variation caused together.

Assuming that the inside of a week variable factor is included in irregular key element, the form of i.e. irregular key element is ID_r, it is assumed that ID is decomposited from former sequence_r, obtain Monday with regression analysis, two ..., the respective weights of day, thus by ID_rIt is decomposed into true Positive irregular key element I and the inside of a week variable factor D_r。

${\mathrm{ID}}_{\mathrm{rt}}\text{-1.0=}\frac{x_{1t}{B}_1+x_{2t}{B}_2+\·\·\·x_{7t}{B}_t}{A_t}+I_t,$

In above formula: ID_rtFor including t month the inside of a week variable factor D_rIrregular key element；x_itFor week i in the t month Natural law (t=1 ..., n)；B_iFor week i weight ()；A_tFor the natural law of the t month, take 28.25 days February；I_tFor really Irregular key element.

At thus obtained B_iEstimated value be b_iTime, the inside of a week variable factor of the t month can be calculated according to equation below D_r:

D_rt={x_1t(b₁+1)+x_2t(b₂+1)+…+x_7t(b₇+1)}/A_t。

(3) after data sequence being adjusted according to festivals or holidays and the inside of a week factor, the special item in sequence is entered Row is revised.

When decomposing the various factors in economic time series, need to revise in advance have in erratic variation the most different The item (the most special item, such as strike, the impact of awful weather, error in data etc.) of constant value.Its method is:

The setting of the boundary value of the most special item.

Assume to decomposite from former sequence irregular key element I.In order to get rid of the exceptional value in Irregular variation I, Need to calculate 5 years rolling average standard deviations of I.First initial 5 year rolling average standard deviation is calculatedThat is:

${\mathrm{\σ}}_j^0=\sqrt{\frac{1}{60}\underset{t=j\×12-36+1}{\overset{j\×12+24}{\mathrm{\Σ}}}{(I_t-{\stackrel{\‾}{I}}_j)}^2},$ j=3,4,…,m-2

In above formulaBeing 5 years moving averages of I sequence, m is the year number of I sequence, t=1,2 ..., n(t is the moon of I sequence Number).AllowCorresponding to the center year during 5 years, calculate one every yearThereforeIt it is year degree series.It is believed that it is full FootI_tIt is special, removes these I_t, by following formula:

${\mathrm{\σ}}_j=\sqrt{\frac{1}{60-a}\mathrm{\Σ}{(I_t-{\stackrel{\‾}{I}}_j)}^2},$ J=3,4 ..., m-2,

Again calculate 5 years mobile standard deviation { σ_j, t=1 in formula, 2 ..., n(t is the moon number of I sequence), a is special datum Number.{σ_jSequence two ends respectively lack 2, are respectively adopted distance top and the terminal { σ of the 3rd year_jReplace that two ends are short of two The σ in year_jValue.

The correction of the most special item.

According to 5 years mobile standard deviation { σ_jCalculate flexible strategy w of correction,

Wherein, t=1,2 ..., n(t is the moon number of I sequence), j= 1,2 ..., m, utilize above-mentioned inequality can revise the special item of I sequence: corresponding to w_t< It of 1, with this w_tFor flexible strategy, therewith phase The I of each two before and after Jin_t-2,I_t-1, I_t+1, I_t+2(notice that the w corresponding to item taken is necessarily equal to 1, otherwise take side Value) totally 5 make weighted average, by the value so obtainedReplace I_t.If corresponding to w_t< the I of 1_tWhen being positioned at two ends, with this w_tFor Power, 3 ws close with it_tThe I value of=1 totally 4 make weighted average, by this meansigma methods obtainedReplace I_t.Revise special item After I sequence be designated as I^w。

(4) according to monthly data sequenceCarry out initial estimation, obtain initial trend circulating component.

Use the trend circulating component in 12 rolling average estimated sequences of centralization

${\mathrm{TC}}_t^{\left(1\right)}=\frac{1}{2}(\frac{y_{t-6}+y_{t-5}+\&CenterDot;\&CenterDot;\&CenterDot;+y_t+\&CenterDot;\&CenterDot;\&CenterDot;+y_{t+5}}{12}+\frac{y_{t-5}+\&CenterDot;\&CenterDot;\&CenterDot;+y_t+\&CenterDot;\&CenterDot;\&CenterDot;+y_{t+5}+y_{t+6}}{12}),$

$=\frac{1}{24}{y}_{t-6}+\frac{1}{12}{y}_{t-5}+\&CenterDot;\&CenterDot;\&CenterDot;+\frac{1}{12}{y}_t+\&CenterDot;\&CenterDot;\&CenterDot;+\frac{1}{12}{y}_{t+5}+\frac{1}{24}{y}_{t+6}$

Wherein, y_t-6, y_t-5..., y_t,…,y_t+5,y_t+6It it is monthly data sequenceIn element.

(5) according to trend circulating componentEstimate irregular composition in season

(6) according to irregular composition in seasonTo application of each month 3 × 3 rolling averages composition in season according to a preliminary estimate:

First according to equation below, seasonal factor is obtained

${\hat{S}}_t^{\left(1\right)}=\frac{1}{3}(\frac{{\mathrm{SI}}_{t-24}^{\left(1\right)}+{\mathrm{SI}}_{t-12}^{\left(1\right)}+{\mathrm{SI}}_t^{\left(1\right)}}{3}+\frac{{\mathrm{SI}}_{t-12}^{\left(1\right)}+{\mathrm{SI}}_t^{\left(1\right)}+{\mathrm{SI}}_{t+12}^{\left(1\right)}}{3}+\frac{{\mathrm{SI}}_t^{\left(1\right)}+{\mathrm{SI}}_{t+12}^{\left(1\right)}+{\mathrm{SI}}_{t+24}^{\left(1\right)}}{3});$

$\frac{1}{9}{\mathrm{SI}}_{t-24}^{\left(1\right)}+\frac{2}{9}{\mathrm{SI}}_{t-12}^{\left(1\right)}+\frac{3}{9}{\mathrm{SI}}_t^{\left(1\right)}+\frac{2}{9}{\mathrm{SI}}_{t+12}^{\left(1\right)}+\frac{1}{9}{\mathrm{SI}}_{t+24}^{\left(1\right)}$

Then it is standardized seasonal factor calculating, obtains standard seasonal factorSo that factor sum is each Individual continuous print is all approximately zero in 12 months:

$S_t^{\left(1\right)}={\hat{S}}_t^{\left(1\right)}-(\frac{1}{24}{\hat{S}}_{t-6}^{\left(1\right)}+\frac{1}{12}{\hat{S}}_{t-5}^{\left(1\right)}+\&CenterDot;\&CenterDot;\&CenterDot;+\frac{1}{12}{\hat{S}}_{t+5}^{\left(1\right)}+\frac{1}{24}{\hat{S}}_{t+6}^{\left(1\right)}).$

(7) according to standard seasonal factorSequence after first estimation seasonal adjustment

(8) trend circulating component is estimated further with 13 rolling averages

${\mathrm{TC}}_t^{\left(2\right)}=\frac{1}{16796}(-{375\mathrm{TCI}}_{t-6}^{\left(1\right)}-{468\mathrm{TCI}}_{t-5}^{\left(1\right)}+{1100\mathrm{TCI}}_{t-3}^{\left(1\right)}+2475\mathrm{TC}{I}_{t-2}^{\left(1\right)}+{3600\mathrm{TCI}}_{t-1}^{\left(1\right)}$

$+{4032\mathrm{TCI}}_t^{\left(1\right)}+{3600\mathrm{TCI}}_{t+1}^{\left(1\right)}+{2475\mathrm{TCI}}_{t+2}^{\left(1\right)}+{1100\mathrm{TCI}}_{t+3}^{\left(1\right)}-{468\mathrm{TCI}}_{t+5}^{\left(1\right)}-{325\mathrm{TCI}}_{t+6}^{\left(1\right)}).$

(9) irregular composition in season is estimated further:

(10) with the composition in season that 3 × 5 rolling averages estimations are final:

Final seasonal factor is obtained according to equation below

${\hat{S}}_t^{\left(2\right)}=\frac{1}{15}{\mathrm{SI}}_{t-36}^{\left(2\right)}+\frac{2}{15}{\mathrm{SI}}_{t-24}^{\left(2\right)}+\frac{3}{15}{\mathrm{SI}}_{t-12}^{\left(2\right)}+\frac{3}{15}{\mathrm{SI}}_t^{\left(2\right)}+\frac{3}{15}{\mathrm{SI}}_{t+12}^{\left(2\right)}+\frac{2}{15}{\mathrm{SI}}_{t+24}^{\left(2\right)}+\frac{1}{15}{\mathrm{SI}}_{t+36}^{\left(2\right)}$

Then to seasonal factorIt is standardized obtaining standardized final seasonal factorFactor sum is existed Each continuous print is approximately zero in 12 months:

$S_t^{\left(2\right)}={\hat{S}}_t^{\left(2\right)}-(\frac{1}{24}{\hat{S}}_{t-6}^{\left(2\right)}+\frac{1}{12}{\hat{S}}_{t-5}^{\left(2\right)}+\&CenterDot;\&CenterDot;\&CenterDot;+\frac{1}{12}{\hat{S}}_{t+5}^{\left(2\right)}+\frac{1}{24}{\hat{S}}_{t+6}^{\left(2\right)}).$

(11) according to standardized final seasonal factorEstimate the sequence after seasonal adjustment

(12) obtaining final trend circulating component, its method is as follows:

First irregular composition is estimated:

Then irregular item is usedAnd trend termThe moon rate of increase the ratio of absolute value sum weigh irregularly The significance of composition

$\stackrel{\&OverBar;}{I}/\stackrel{\&OverBar;}{C}=\frac{\underset{t=2}{\overset{n}{\mathrm{\&Sigma;}}}|I_t^{\left(2\right)}/{I_{t-1}^{\left(2\right)}}^{-1}|}{\underset{t=2}{\overset{n}{\mathrm{\&Sigma;}}}\left|{\mathrm{TC}}_t^{\left(2\right)}{{\mathrm{TC}}_{t-1}^{\left(2\right)}}^{-1}\right|},$

IfThen with the trend circulating component that following 9 rolling averages estimation is final:

${\mathrm{TC}}_t^{\left(3\right)}=\frac{1}{2431}(-{99\mathrm{TCI}}_{t-4}^{\left(2\right)}-{24\mathrm{TCI}}_{t-3}^{\left(2\right)}+{288\mathrm{TCI}}_{t-2}^{\left(2\right)}+{348\mathrm{TCI}}_{t-1}^{\left(2\right)}+{805\mathrm{TCI}}_t^{\left(2\right)};$

$+648{\mathrm{TCI}}_{t+1}^{\left(2\right)}+{288\mathrm{TCI}}_{t+2}^{\left(2\right)}-{24\mathrm{TCI}}_{t+3}^{\left(2\right)}-{99\mathrm{TCI}}_{t+4}^{\left(2\right)})$

IfThen with the trend circulating component that following 13 rolling averages estimation is final:

${\mathrm{TC}}_t^{\left(3\right)}=\frac{1}{16796}(-{325\mathrm{TCI}}_{t-6}^{\left(2\right)}-468\mathrm{TC}{I}_{t-5}^{\left(2\right)}+{1100\mathrm{TCI}}_{t-3}^{\left(2\right)}+{2475\mathrm{TCI}}_{t-2}^{\left(2\right)}+{3600\mathrm{TCI}}_{t-1}^{\left(2\right)};$

$+{4032\mathrm{TCI}}_t^{\left(2\right)}+{3600\mathrm{TCI}}_{t+1}^{\left(2\right)}+{2475\mathrm{TCI}}_{t+2}^{\left(2\right)}+{1100\mathrm{TCI}}_{t+3}^{\left(2\right)}-{468\mathrm{TCI}}_{t+5}^{\left(2\right)}-{325\mathrm{TCI}}_{t+6}^{\left(2\right)})$

IfThen with the trend circulating component that following 23 rolling averages estimation is final:

${\mathrm{TC}}_t^{\left(3\right)}=\frac{1}{4032015}(-{17250\mathrm{TCI}}_{t-11}^{\left(2\right)}-{44022\mathrm{TCI}}_{t-10}^{\left(2\right)}-{63250\mathrm{TCI}}_{t-9}^{\left(2\right)}-{5757\mathrm{TCI}}_{t-8}^{\left(2\right)}-{19950\mathrm{TCI}}_{t-7}^{\left(2\right)})$

$+{54150\mathrm{TCI}}_{t-6}^{\left(2\right)}+156978{\mathrm{TCI}}_{t-5}^{\left(2\right)}+{275400\mathrm{TCI}}_{t-4}^{\left(2\right)}+{392700\mathrm{TCI}}_{t-3}^{\left(2\right)}+{491700\mathrm{TCI}}_{t-2}^{\left(2\right)}+{557700\mathrm{TCI}}_{t-1}^{\left(2\right)},$

$+580853{\mathrm{TCI}}_t^{\left(2\right)}+{557700\mathrm{TCI}}_{t+1}^{\left(2\right)}+{491700\mathrm{TCI}}_{t+2}^{\left(2\right)}+{392700\mathrm{TCI}}_{t+3}^{\left(2\right)}+{275400\mathrm{TCI}}_{t+4}^{\left(2\right)}+{156978\mathrm{TCI}}_{t+5}^{\left(2\right)}$

$+54150{\mathrm{TCI}}_{t+6}^{\left(2\right)}-{19950\mathrm{TCI}}_{t+7}^{\left(2\right)}-{58575\mathrm{TCI}}_{t+8}^{\left(2\right)}-{63250\mathrm{TCI}}_{t+9}^{\left(2\right)}-{44022\mathrm{TCI}}_{t+10}^{\left(2\right)}-{17250\mathrm{TCI}}_{t+11}^{\left(2\right)})$

Final trend circulating component is got through above-mentioned calculating

(13) according to trend circulating componentWith the sequence after seasonal adjustmentEstimate final irregular composition: $I_t^{\left(3\right)}={\mathrm{TCI}}_t^{\left(2\right)}-{\mathrm{TC}}_t^{\left(3\right)}.$

Then get sequence Y only comprising trend circulation item after seasonal adjustment⁽²⁾=TC⁽³⁾。

In above-described embodiment of the application, in Fig. 4, step S108 realizes especially by following method: to physical quantity and valency Value figureofmerit, the development index of the sequence after calculating seasonal adjustment: It is the index of development index, Do not convert, i.e. Y⁽³⁾=Y⁽²⁾。

In above-described embodiment of the application, perform step S110: according to step-out time analysis model and/or K-L quantity of information mould Type carries out correlation calculations to warning index, to obtain the relative coefficient between each selected index and reference index, and According to relative coefficient, selected index is screened, include walking as follows obtaining the process of leading indicators and coincidence indicator Rapid:

The relative coefficient r between each selected index and reference index is obtained according to equation below_l:

Wherein, l=0, ± 1, ± 2 ..., ± 12, Y=(y₁,y₂,…,y_nOn the basis of) Index, X=(x₁,x₂,…,x_n) for being chosen index, l is the time difference, is in particular advanced or lag period, n_lFor all indexs Number；By time difference value in the first span and relative coefficient r_lMore than first threshold selected index as in advance refer to Mark, and by time difference value in the second span and relative coefficient r_lMore than the selected index of Second Threshold as unanimously Index.Wherein, the first span can be less than-3, and first threshold can be 0.7, and the second span may be greater than In-2 and less than or equal to 2, Second Threshold can be 0.7.

Specifically, using reference index as " mark post " of screening in this step, model Preliminary screening is analyzed in used time difference correlation Coincidence indicator and leading indicators.All selected the index the most advanced or delayed l phase in addition to reference index is (i.e. in above-described embodiment The time difference) (l=0, ± 1, ± 2 ..., ± 12), calculate the dependency of each selected index and reference index the most respectively Coefficient r_l:

$r_l=\frac{\underset{t=1}{\overset{n_l}{\mathrm{\&Sigma;}}}(x_{t+l}-\stackrel{\&OverBar;}{x})(y_t-\stackrel{\&OverBar;}{y})}{\sqrt{\underset{t=1}{\overset{n_l}{\mathrm{\&Sigma;}}}{(x_{t+l}-\stackrel{\&OverBar;}{x})}^2{(y_t-\stackrel{\&OverBar;}{y})}^2}},$ L=0, ± 1, ± 2 ..., ± 12,

Y=(y in above formula₁,y₂,…,y_nIndex on the basis of), X=(x₁,x₂,…,x_n) for being chosen index, r is phase relation Number, l represents the advanced or lag period (i.e. the time difference), and l represents advanced when taking negative, represents delayed when taking positive number, l be referred to as the time difference or Postpone number,WithIt is respectively sequence X and the meansigma methods of Y.n_lIt it is the data amount check of all indexs.Then maximum time difference correlation coefficient Being considered the time difference dependency relation reflecting selected index with reference index, the corresponding number l that postpones represents the advanced or lag period, i.e. Make time difference relative coefficient r_lMaximum delay number l is exactly this selected index and the advanced of reference index or lag period.

Fig. 5 is the method schematic diagram of the acquisition coincidence indicator according to embodiment illustrated in fig. 4 and leading indicators, specifically, Get relative coefficient r_lAfterwards, by the method acquisition leading indicators shown in Fig. 5 and coincidence indicator:

Step S302: the Testing index data whether time difference<-3 and relative coefficient>0.7 or achievement data whether-2≤ The time difference≤2 and relative coefficient > 0.7.Wherein, at the time difference<-3 and relative coefficient>0.7 or the achievement data of achievement data Eligible-2≤time difference≤2 and relative coefficient > in the case of 0.7, perform step S304, do not meet the time difference at achievement data <-3 and relative coefficient>0.7, and do not meet-2≤time difference≤2 and relative coefficient>in the case of 0.7, perform step S310: Abandon achievement data.

Step S304: obtain initial leading indicators and initial coincidence indicator, wherein, in the time difference <-3 and the phase of achievement data Close property coefficient > it is initial leading indicators by this data decimation in the case of 0.7, in-2≤time difference≤2 of achievement data and relevant Property coefficient > in the case of 0.7, choosing this achievement data is initial coincidence indicator.

In above-described embodiment of the application, according to step-out time analysis model and/or K-L information computation, warning index is entered Row correlation calculations, with the relative coefficient between each selected index and reference index, and according to relative coefficient to quilt Selective goal screens, and includes obtaining the step of leading indicators and coincidence indicator: obtain each selected according to equation below Select the relative coefficient r between index and reference index_l:

Wherein, l=0, ± 1, ± 2 ..., ± 12, Y=(y₁,y₂,…,y_nOn the basis of) Index, X=(x₁,x₂,…,x_n) for being chosen index, l is the time difference, n_lNumber for all indexs；Time difference value is taken first In the range of value and relative coefficient r_lMore than the selected index of first threshold as the Raw performance of leading indicators, and by the time difference Value is in the second span and relative coefficient r_lMore than initial as coincidence indicator of the selected index of Second Threshold Index；It is standardized processing to the Raw performance of reference index, the Raw performance of leading indicators and coincidence indicator, to obtain Standard basis index series p_t, standard be chosen sequence q of index_t, wherein, standard is chosen index and includes standard leading indicators And standard coincidence indicator；Obtain the K-L information that each standard is chosen between index and standard basis index as follows Amount k_l:

k_l=∑p_tln(p_t/q_t+1), wherein, l=0, ± 1 ..., ± 12, T=1, 2 ..., n, l are the time difference, n_lNumber for all indexs；By time difference value in the 3rd span and K-L quantity of information k_lIt is less than The standard of the 3rd threshold value is chosen index as leading indicators, and by time difference value in the 4th span and K-L quantity of information k_lLess than the selected index of the 4th threshold value as coincidence indicator.Wherein, the 3rd span can be less than-3, the 3rd threshold value Can be 0.3, the 4th span may be greater than equal to-2 and less than or equal to 2, and the 4th threshold value can be 0.3.

Said method can be achieved by the steps of: reference index is done standardization, the standard basis after process Index series is designated as p_t:

$p_t=y_t/\underset{t=1}{\overset{n}{\mathrm{\&Sigma;}}}{y}_t,$ T=1,2 ..., n,

Leading indicators and the coincidence indicator of primary election are also done standardization, and the standard after process is chosen the sequence of index It is designated as q_t:

$q_t=x_t/\underset{t=1}{\overset{n}{\mathrm{\&Sigma;}}}{x}_t,$ T=1,2 ..., n,

Then, the K-L quantity of information k about reference index after each primary election beacon delay l it is calculated as follows_l:

k_l=∑p_tln(p_t/q_t+l)=0, ± 1 ..., ± 12

Wherein, l represents the advanced or lag period, and l represents advanced when taking negative, represents delayed, when l is referred to as when taking positive number Difference, n_lBeing the data amount check (numbers of the most all indexs) after data are evened up, the t in above-mentioned formula represents month, and i represents week Number.

After calculating 2L+1 K-L quantity of information, from k_lValue is selected minima k_l′As selected index x about base The K-L quantity of information of quasi-index y, i.e.Its corresponding delay number l^*It is exactly that selected index is optimal advanced or stagnant Rear moon number (season), wherein, K-L quantity of information is closer to 0, illustrate index x and reference index y closer to.

Specifically can screen leading indicators as follows and coincidence indicator is designated as W respectively⁽³⁾And Z⁽³⁾:

Step S306: detect the initial leading indicators whether time difference <-3 and K-L quantity of information < and 0.3 or initial consistent data be No-2≤time difference≤2 and K-L quantity of information < 0.3.Wherein, initial leading indicators meet the time difference <-3 and K-L quantity of information < 0.3 or The initial consistent data of person meets-2≤time difference≤2 and K-L quantity of information < in the case of 0.3, performs step S308, initial leading Index do not meet the time difference <-3 and K-L quantity of information < and 0.3, perform step S310: abandon achievement data, be not inconsistent at initial consistent data Close-2≤time difference≤2 and K-L quantity of information < in the case of 0.3, performs step S310: abandon achievement data.

Step S308: selected leading indicators and coincidence indicator.Wherein, in the time difference <-the 3 and K-L information of initial leading indicators < in the case of 0.3, this index selected is existing index to amount, at initial coincidence indicator data ,-2≤time difference≤2 and K-L quantity of information < in the case of 0.3, this index selected is coincidence indicator.Such as: choosing delay number l=-2, the index of-3 ,-4 is as the elder generation of primary election Row index, postpones number l=-1, and the index of 0,1 is as the coincidence indicator of primary election.

According to above-described embodiment of the application, according to composite index number model, leading indicators and coincidence indicator are synthesized, Include using acquisition as the leading composite index number of early-warning parameters and the step of coincident composite Index: to leading indicators and coincidence indicator Carry out symmetrical change process respectively, to obtain leading indicators symmetry rate of change C_{W, i}(t) and coincidence indicator symmetry rate of change C_{Z, i} T (), wherein, carries out symmetrical change process by equation below to leading indicators, to obtain leading indicators symmetry rate of change C_w,i (t):

Wherein,Be i-th (i=1,2 ..., k_w) individual leading indicators, t=2, 3 ..., n, k_wThe number of leading indicators；By equation below, coincidence indicator is carried out symmetrical change process, to obtain coincidence indicator Symmetrical rate of change C_z,i(t):

Wherein,Be i-th (i=1,2 ..., k_z) individual coincidence indicator, t=2, 3 ..., n, k_zIt it is the number of coincidence indicator；To leading indicators symmetry rate of change C_w,i(t) and coincidence indicator symmetry rate of change C_z,i T result that () is standardized processing and obtaining after trend adjustment carries out composite calulation, to obtain leading composite index number and Cause composite index number.Wherein, the t in above-mentioned formula represents month.

Specifically, the symmetrical rate of change of index is sought and by its standardization with index synthetic model, according to following formula respectively RightWithSeek symmetrical rate of change C_w,i(t) and C_z,i(t):

$C_{w,i}\left(t\right)=200\&times;\frac{W_i^{\left(3\right)}\left(t\right)-W_i^{\left(3\right)}(t-1)}{W_i^{\left(3\right)}\left(t\right)+W_i^{\left(3\right)}(t-1)},$ T=2,3 ..., n,

T=2,3 ..., n, wherein,Be i-th (i=1,2 ..., k_w) individual elder generation Row index,Be i-th (i=1,2 ..., k_z) individual coincidence indicator, k_wAnd k_zIt is the number of leading indicators and coincidence indicator respectively.

In above-described embodiment of the application, to leading indicators symmetry rate of change C_w,i(t) and coincidence indicator symmetry rate of change C_z,iT () is standardized processing and after trend adjustment, the result that obtains carries out composite calulation, with obtain leading composite index number and The step of coincident composite Index includes: obtain normalization factor A by equation below_{W, i}And A_{Z, i}: t=2,3,…,n；Use normalization factor A_{W, i}And A_{Z, i}Respectively by leading indicators symmetry rate of change C_w,i(t) With coincidence indicator symmetry rate of change C_z,iT () is standardized processing, to obtain standardization rate of change S_w,i(t) and S_z,i(t), its In,

$S_{w,i}\left(t\right)=\frac{C_{w,i}\left(t\right)}{A_{w,i}},$ $S_{z,i}\left(t\right)=\frac{C_{z,i}\left(t\right)}{A_{z,i}},$ t=2,3,…,n；

To standardization rate of change S_w,i(t) and S_z,i(t) be averaged rate of change process, to obtain the standardization of leading indicators Average rate of change V_wStandardization average rate of change V of (t) and coincidence indicator_z(t)；Standardization mean change according to leading indicators Rate V_wStandardization average rate of change V of (t) and coincidence indicator_zT () carries out composite calulation, to obtain leading composite index number I_w(t) and Coincident composite Index I_z(t), wherein, $I_w\left(t\right)=I_w(t-1)\&times;\frac{200+V_w\left(t\right)}{200-V_w\left(t\right)},$ $I_z\left(t\right)=I_z(t-1)\&times;\frac{200+V_z\left(t\right)}{200-V_z\left(t\right)},$ And I_w(1)= 100, I_z(1)=100。

Specifically, according to equation below to leading indicators symmetry rate of change C_w,i(t) and coincidence indicator symmetry rate of change C_z,i T () does standardized calculation so that it is average absolute value is equal to 1, obtains normalization factor A_w,iAnd A_{Z, i}:

I_t, $A_{z,i}=\underset{t=2}{\overset{n}{\mathrm{\&Sigma;}}}\frac{\left|C_{z,i}\left(t\right)\right|}{n-1},$ T=2,3 ..., n,

Then according to A_w,iAnd A_{Z, i}Respectively by C_w,i(t) and C_{Z, i}T () standardization, obtains standardization rate of change S_w,i(t) and S_z,i(t):

T=2,3 ..., n, afterwards according to standardization rate of change S_w,i(t) and S_z,i (t) be averaged rate of change process, to obtain standardization average rate of change V of leading indicators_wThe standardization of (t) and coincidence indicator Average rate of change V_z(t), and according to standardization average rate of change V of leading indicators_wT the standardization of () and coincidence indicator averagely becomes Rate V_zT () carries out composite calulation, to obtain leading composite index number I_w(t) and coincident composite Index I_z(t), specifically: I_w(1)= 100, I_z(1)=100, then

$I_w\left(t\right)=I_w(t-1)\&times;\frac{200+V_w\left(t\right)}{200-V_w\left(t\right)},$ $I_z\left(t\right)=I_z(t-1)\&times;\frac{200+V_z\left(t\right)}{200-V_z\left(t\right)}.$

More specifically, getting leading composite index number I_w(t) and coincident composite Index I_zAfter (t), electric power can be needed Asking trend to be adjusted, method is as follows:

According to following compound interest formula each sequence of coincidence indicator group obtained respectively respective average rate of increase:

$r_i=(\sqrt[m_i]{C_{\mathrm{Li}}/C_{\mathrm{Ii}}}-1)\&times;100,$ I=1,2 ..., k_z,

Wherein, as shown in Figure 6, t is month,WithIt is consistent respectively Index group i-th index at first with the most metacyclic meansigma methods, m_IiWith m_LiBe respectively coincidence indicator group i-th index at first with The most metacyclic moon number, k₂It is coincidence indicator number, m_iIt is the center circulated at first to the moon number between the most metacyclic center.

Then the average rate of increase G of coincidence indicator group is obtained_r, and as target trend:Afterwards To the leading and initial composite index number I of coincidence indicator_w(t) and I_zT () obtains their respective balanced growth with compound interest formula respectively Rate r '_wWith r '_z:

$r_w^{\&prime;}(\sqrt[m_w]{C_{\mathrm{Lw}}/C_{\mathrm{Iw}}}-1)\&times;100,$ $r_z^{\&prime;}(\sqrt[m_z]{C_{\mathrm{Lz}}/C_{\mathrm{Iz}}}-1)\&times;100,$

Wherein,

Standardization average rate of change V to leading indicators group and coincidence indicator group again_w(t) and V_zT () does trend adjustment:

V′_w(t)=V_w(t)+(Gr-r′_w), V '_z(t)=V_z(t)+(Gr-r′_z), t=2,3 ..., n.Then according to above-mentioned enforcement Method in example calculates composite index number: make I '_w(1)=100, I '_z(1)=100, then

${I^{\&prime;}}_w\left(t\right)={I^{\&prime;}}_w(t-1)\&times;\frac{200+{V^{\&prime;}}_w\left(t\right)}{200-{V^{\&prime;}}_w\left(t\right)},$ ${I^{\&prime;}}_z\left(t\right)={I^{\&prime;}}_z(t-1)\&times;\frac{200+{V^{\&prime;}}_z\left(t\right)}{200-{V^{\&prime;}}_z\left(t\right)},$

Generate with leading composite index number CI that the benchmark time is 100_w(t) and coincident composite Index CI_z(t):

${\mathrm{CI}}_w\left(t\right)=(I_w^{\&prime;}\left(t\right)/\stackrel{\&OverBar;}{I_w^{\&prime;}}\&times;100),$ ${\mathrm{CI}}_z\left(t\right)=(I_z^{\&prime;}\left(t\right)/\stackrel{\&OverBar;}{I_z^{\&prime;}})\&times;100,$

WhereinWithIt is I ' respectively_w(t) and I '_zT () is in the meansigma methods in benchmark time.

According to above-described embodiment of the application, to standardization rate of change S_w,i(t) and S_z,iT () is averaged at rate of change Reason, to obtain standardization average rate of change V of leading indicators_wStandardization average rate of change V of (t) and coincidence indicator_zThe step of (t) Suddenly include:

By equation below respectively by the standardization rate of change S of leading indicators_w,iThe standardization change of (t) and coincidence indicator Rate S_z,i(t) be averaged rate of change process, to obtain average rate of change R of leading indicators_wThe average change of (t) and coincidence indicator Rate R_z(t):

$R_w\left(t\right)=\frac{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{S}_{w,i}\left(t\right){\mathrm{\&lambda;}}_{w,i}}{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{w,i}},$ $R_z\left(t\right)=\frac{\underset{i=1}{\overset{k_z}{\mathrm{\&Sigma;}}}{S}_{z,i}\left(t\right){\mathrm{\&lambda;}}_{z,i}}{\underset{i=1}{\overset{k_z}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{z,i}},$ Wherein, λ_w,iAnd λ_z,iIt is that leading indicators refers to consistent respectively The weight of target i-th index；Criterion factor F is obtained by equation below_w:

$F_w=\left[\underset{t=2}{\overset{n}{\mathrm{\&Sigma;}}}\right|R_w\left(t\right)|/(n-1)]/\left[\underset{t=2}{\overset{n}{\mathrm{\&Sigma;}}}\right|R_z\left(t\right)|/(n-1)];$ According to criterion factor F_wIt is standardized average Rate of change processes, to obtain standardization average rate of change V of leading indicators_wStandardization average rate of change V of (t) and coincidence indicator_z (t), wherein, V_w(t)=R_w(t)/F_w, V_z(t)=R_z(t)。

Specifically, initial composite index number I is being obtained_w(t) and I_zBefore (t), by equation below respectively by leading indicators Standardization rate of change S_w,iThe standardization rate of change S of (t) and coincidence indicator_z,iT the () rate of change that is averaged processes, to obtain in advance Average rate of change R of index_wAverage rate of change R of (t) and coincidence indicator_z(t):

$R_w\left(t\right)=\frac{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{S}_{w,i}\left(t\right){\mathrm{\&lambda;}}_{w,i}}{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{w,i}},$ $R_z\left(t\right)=\frac{\underset{i=1}{\overset{k_z}{\mathrm{\&Sigma;}}}{S}_{z,i}\left(t\right){\mathrm{\&lambda;}}_{z,i}}{\underset{i=1}{\overset{k_Z}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{z,i}},$ λ_w,iAnd λ_z,iIt is in advance and the i-th of coincidence indicator group respectively The weight of individual index.

Then according to equation below gauge index normalization factor F_w:

$F_w=\left[\underset{t=2}{\overset{n}{\mathrm{\&Sigma;}}}\right|R_w\left(t\right)|/(n-1)]/\left[\underset{t=2}{\overset{n}{\mathrm{\&Sigma;}}}\right|R_z\left(t\right)|/(n-1)];$

Finally according to criterion factor F_wIt is standardized the average rate of change to process, to obtain the standard of leading indicators Change average rate of change V_wStandardization average rate of change V of (t) and coincidence indicator_z(t):

V_w(t)=R_w(t)/F_w, V_z(t)=R_z(t), t=2,3 ..., n, wherein, by the average rate of change of coincidence indicator sequence Amplitude goes to adjust leading indicators sequence and the average rate of change of lagging indicator sequence, its purpose is to two indexes as one Individual harmonious system is applied.

It should be noted that can be at such as one group of computer executable instructions in the step shown in the flow chart of accompanying drawing Computer system performs, and, although show logical order in flow charts, but in some cases, can be with not It is same as the step shown or described by order execution herein.

As can be seen from the above description, present invention achieves following technique effect: by the acquisition electric power of the application The method and device of the early-warning parameters of demand, after the trend term obtained in original data sequence and periodic term, passes through logarithm Obtain leading indicators and coincidence indicator according to sequence screening and calculating, then above-mentioned leading indicators and coincidence indicator index are synthesized Model synthesis obtain early-warning parameters, and according to early-warning parameters analyze power demand cycle fluctuation, solve in prior art Electricity needs field uses Predicting Technique to obtain short-term cyclic fluctuation distortion, it is impossible to acquire the early warning meeting electricity needs Parameter, thus cause formulating the measure reasonably tackling cyclic fluctuation according to power cycle fluctuation, it is achieved that accurately Obtain the early-warning parameters of electricity needs, thus formulate the effect of the counter-measure of reasonable science accurately according to short-term cyclic fluctuation Really, and then slow down the amplitude of cyclic swing, reduce the destructiveness that power industry and economic development are caused by cyclic swing.

Obviously, those skilled in the art should be understood that each module of the above-mentioned present invention or each step can be with general Calculating device realize, they can concentrate on single calculating device, or be distributed in multiple calculating device and formed Network on, alternatively, they can with calculate the executable program code of device realize, it is thus possible to by they store Performed by calculating device in the storage device, or they are fabricated to respectively each integrated circuit modules, or by them In multiple modules or step be fabricated to single integrated circuit module and realize.So, the present invention be not restricted to any specifically Hardware and software combines.

The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for the skill of this area For art personnel, the present invention can have various modifications and variations.All within the spirit and principles in the present invention, that is made any repaiies Change, equivalent, improvement etc., should be included within the scope of the present invention.

Claims (12)

1. the method for the early-warning parameters obtaining electricity needs, it is characterised in that including:

Obtain the data sequence for generating warning index；

According to adjusting parameter, described data sequence is screened, to obtain the data sequence including trend term and periodic term；

The Trend index of the data sequence of described trend term and periodic term is included described in calculating, and according to described Trend index pair The described data sequence including described trend term and periodic term filters, and to obtain warning index sequence, described early warning refers to Mark sequence is that in described data sequence, Trend index is the data increased；

Extract index on the basis of the generated energy in described warning index sequence, and the index that extraction is in addition to described generated energy is quilt Selective goal；

According to step-out time analysis model and/or K-L information computation, described warning index is carried out correlation calculations, each to obtain Relative coefficient between described selected index and described reference index, and be chosen described according to described relative coefficient Index is screened, to obtain leading indicators and coincidence indicator；

According to composite index number model, described leading indicators and coincidence indicator are synthesized, to obtain leading as early-warning parameters Composite index number and coincident composite Index；

After obtaining the data sequence for generating warning index, described method also includes: to the number in described data sequence According to carrying out pretreatment, described pretreatment includes: fill up missing data process, revise noise data process, data smoothing process with And data normalization processes.

Method the most according to claim 1, it is characterised in that utilize step-out time analysis model and/or K-L information computation pair Described warning index carries out correlation calculations, with the dependency system between each described selected index and described reference index Number, and according to described relative coefficient, described selected index is screened, to obtain the step of leading indicators and coincidence indicator Suddenly include:

The relative coefficient r between each described selected index and described reference index is obtained according to equation below_l:

$r_l=\frac{\underset{t=1}{\overset{n_l}{\&Sigma;}}(x_{t+l}-\stackrel{\&OverBar;}{x})(y_t-\stackrel{\&OverBar;}{y})}{\sqrt{\underset{t=1}{\overset{n_l}{\&Sigma;}}{(x_{t+l}-\stackrel{\&OverBar;}{x})}^2{(y_t-\stackrel{\&OverBar;}{y})}^2}},$

Wherein, l=0, ± 1, ± 2 ..., ± 12, Y=(y₁,y₂,…,y_nIndex on the basis of), X=(x₁,x₂,…,x_n) it is selected Select index,WithBeing respectively sequence X and the meansigma methods of Y, l is the time difference, n_lFor the number of all indexs, t=1,2 ..., n is the moon Number, x_t+1For the selected index of the t+1 month, y_tReference index for the t month；

By described time difference value in the first span and described relative coefficient r_lMake more than the selected index of first threshold For described leading indicators, and by described time difference value in the second span and described relative coefficient r_lMore than Second Threshold Selected index as described coincidence indicator.

Method the most according to claim 2, it is characterised in that according to step-out time analysis model and/or K-L information computation pair Described warning index carries out correlation calculations, with the dependency system between each described selected index and described reference index Number, and according to described relative coefficient, described selected index is screened, to obtain the step of leading indicators and coincidence indicator Suddenly include:

The relative coefficient r between each described selected index and described reference index is obtained according to equation below_l:

$r_l=\frac{\underset{t=1}{\overset{n_l}{\&Sigma;}}(x_{t+l}-\stackrel{\&OverBar;}{x})(y_t-\stackrel{\&OverBar;}{y})}{\sqrt{\underset{t=1}{\overset{n_l}{\&Sigma;}}{(x_{t+l}-\stackrel{\&OverBar;}{x})}^2{(y_t-\stackrel{\&OverBar;}{y})}^2}},$

Wherein, l=0, ± 1, ± 2 ..., ± 12, Y=(y₁,y₂,…,y_nIndex on the basis of), X=(x₁,x₂,…,x_n) it is selected Selecting index, l is the time difference, n_lFor the number of all indexs, t=1,2 ..., n is moon number；

By described time difference value in the first span and described relative coefficient r_lMake more than the selected index of first threshold For the Raw performance of described leading indicators, and by described time difference value in the second span and described relative coefficient r_lGreatly In the selected index of Second Threshold as the Raw performance of described coincidence indicator；

The Raw performance of described reference index, the Raw performance of described leading indicators and described coincidence indicator is standardized Process, to obtain standard basis index series p_t, standard be chosen sequence q of index_t, wherein, described standard is chosen index bag Include standard leading indicators and standard coincidence indicator；

Obtain the K-L quantity of information k that each standard is chosen between index and described standard basis index as follows_l:

k_l=∑ p_tln(p_t/q_t+1), wherein, l=0, ± 1 ..., ± 12,T=1, 2 ..., n is moon number, and l is the time difference, n_lNumber for all indexs；

By described time difference value in the 3rd span and described K-L quantity of information k_lIt is chosen to refer to less than the standard of the 3rd threshold value It is denoted as described leading indicators, and by described time difference value in the 4th span and described K-L quantity of information k_lLess than the 4th The selected index of threshold value is as described coincidence indicator.

The most according to the method in claim 2 or 3, it is characterised in that according to composite index number model by described leading indicators and Coincidence indicator synthesizes, and includes using acquisition as the leading composite index number of early-warning parameters and the step of coincident composite Index:

Described leading indicators and described coincidence indicator are carried out respectively symmetrical change process, to obtain leading indicators symmetry rate of change C_w,i(t) and coincidence indicator symmetry rate of change C_z,i(t),

Wherein, by equation below, described leading indicators is carried out symmetrical change process, to obtain the change of described leading indicators symmetry Rate C_w,i(t):

Wherein,Be i-th (i=1,2 ..., k_w) individual leading indicators, t=2, 3 ..., n, k_wNumber for leading indicators；

By equation below, described coincidence indicator is carried out symmetrical change process, to obtain coincidence indicator symmetry rate of change C_z,i (t):

Wherein,Be i-th (i=1,2 ..., k_z) individual coincidence indicator, t=2, 3 ..., n is moon number, k_zIt it is the number of coincidence indicator；

To described leading indicators symmetry rate of change C_w,i(t) and described coincidence indicator symmetry rate of change C_z,iT () is standardized place The result obtained after reason and trend adjustment carries out composite calulation, to obtain described leading composite index number and coincident composite Index.

Method the most according to claim 4, it is characterised in that to described leading indicators symmetry rate of change C_w,iT () is with described Coincidence indicator symmetry rate of change C_z,iT result that () is standardized processing and obtaining after trend adjustment carries out composite calulation, with The step obtaining described leading composite index number and coincident composite Index includes:

Normalization factor A is obtained by equation below_w,iAnd A_z,i:T=2, 3,…,n；

Use described normalization factor A_w,iAnd A_z,iRespectively by described leading indicators symmetry rate of change C_w,i(t) and described consistent finger The symmetrical rate of change C of mark_z,iT () is standardized processing, to obtain standardization rate of change S_w,i(t) and S_z,i(t), wherein,

$S_{w,i}\left(t\right)=\frac{C_{w,i}\left(t\right)}{A_{w,i}},S_{z,i}\left(t\right)=\frac{C_{z,i}\left(t\right)}{A_{z,i}},t=2,3,...,n;$

To described standardization rate of change S_w,i(t) and S_z,i(t) be averaged rate of change process, to obtain the mark of described leading indicators Standardization average rate of change V_wStandardization average rate of change V of (t) and described coincidence indicator_z(t)；

Standardization average rate of change V according to described leading indicators_wStandardization average rate of change V of (t) and described coincidence indicator_z T () carries out composite calulation, to obtain described leading composite index number I_w(t) and coincident composite Index I_z(t), wherein,And I_w(1)=100, I_z(1)=100.

Method the most according to claim 5, it is characterised in that to described standardization rate of change S_w,i(t) and S_z,i(t) carry out The average rate of change processes, to obtain standardization average rate of change V of described leading indicators_wThe standard of (t) and described coincidence indicator Change average rate of change V_zT the step of () including:

By equation below respectively by the standardization rate of change S of described leading indicators_w,iThe standardization of (t) and described coincidence indicator Rate of change S_z,i(t) be averaged rate of change process, to obtain average rate of change R of described leading indicators_wT () is consistent with described Average rate of change R of index_z(t):

Wherein, λ_w,iAnd λ_z,iIt is leading indicators and coincidence indicator respectively The weight of i-th index；

Criterion factor F is obtained by equation below_w:

$F_w=\&lsqb;\underset{t=2}{\overset{n}{\&Sigma;}}\left|R_w\left(t\right)\right|/(n-1)\&rsqb;/\&lsqb;\underset{t=2}{\overset{n}{\&Sigma;}}\left|R_z\left(t\right)\right|/(n-1)\&rsqb;;$

According to described criterion factor F_wIt is standardized the average rate of change to process, to obtain the standard of described leading indicators Change average rate of change V_wStandardization average rate of change V of (t) and described coincidence indicator_z(t), wherein, V_w(t)=R_w(t)/F_w, V_z (t)=R_z(t)。

7. the device of the early-warning parameters obtaining electricity needs, it is characterised in that including:

First acquisition module, for obtaining the data sequence for generating warning index；

First processing module, for according to adjust parameter described data sequence is screened, with obtain include trend term and The data sequence of periodic term；

First computing module, is used for including described in calculating the Trend index of the data sequence of described trend term and periodic term, and According to described Trend index, the described data sequence including described trend term and periodic term is filtered, refer to obtaining early warning Mark sequence, described warning index sequence is that in described data sequence, Trend index is the data increased；

First extraction module, for extracting index on the basis of the generated energy in described warning index sequence, and extracts except described Index beyond electricity is for being chosen index；

Second computing module, for being correlated with to described warning index according to step-out time analysis model and/or K-L information computation Property calculate, to obtain the relative coefficient between each described selected index and described reference index, and according to described relevant Described selected index is screened by property coefficient, to obtain leading indicators and coincidence indicator；

Second processing module, for synthesizing described leading indicators and coincidence indicator according to composite index number model, to obtain Leading composite index number and coincident composite Index as early-warning parameters；

Described device also includes: the tenth sub-processing module, for the data in described data sequence are carried out pretreatment, described pre- Process includes: fills up missing data and processes, revises noise data process, data smoothing process and data normalization process.

Device the most according to claim 7, it is characterised in that described second computing module includes:

First sub-computing module, for obtaining between each described selected index and described reference index according to equation below Relative coefficient r_l:

Wherein, l=0, ± 1, ± 2 ..., ± 12, Y=(y₁,y₂,…,y_nRefer on the basis of) Mark, X=(x₁,x₂,…,x_n) for being chosen index,WithBeing respectively sequence X and the meansigma methods of Y, l is the time difference, n_lFor all fingers Target number, t=1,2 ..., n is moon number, x_t+1For the selected index of the t+1 month, y_tReference index for the t month；

First sub-processing module, for by described time difference value in the first span and described relative coefficient r_lMore than The selected index of one threshold value is as described leading indicators, and by described time difference value in the second span and described relevant Property coefficient r_lMore than the selected index of Second Threshold as described coincidence indicator.

Device the most according to claim 8, it is characterised in that described second computing module includes:

Second sub-computing module, for obtaining between each described selected index and described reference index according to equation below Relative coefficient r_l:

Wherein, l=0, ± 1, ± 2 ..., ± 12, Y=(y₁,y₂,…,y_nRefer on the basis of) Mark, X=(x₁,x₂,…,x_n) for being chosen index, l is the time difference, n_lFor the number of all indexs, t=1,2 ..., n is month Number；

Second sub-processing module, for by described time difference value in the first span and described relative coefficient r_lMore than The selected index of one threshold value is as the Raw performance of described leading indicators, and by described time difference value in the second span And described relative coefficient r_lMore than the selected index of Second Threshold as the Raw performance of described coincidence indicator；

3rd sub-processing module, for described reference index, the Raw performance of described leading indicators and described coincidence indicator Raw performance be standardized process, to obtain standard basis index series p_t, standard be chosen sequence q of index_t, wherein, Described standard is chosen index and includes standard leading indicators and standard coincidence indicator；

3rd sub-computing module, is chosen between index and described standard basis index for obtaining each standard as follows K-L quantity of information k_l:

k_l=∑ p_t ln(p_t/q_t+1), wherein, l=0, ± 1 ..., ± 12, T= 1,2 ..., n is moon number, and l is the time difference, n_lNumber for all indexs；

4th sub-processing module, for by described time difference value in the 3rd span and described K-L quantity of information k_lLess than the 3rd The standard of threshold value is chosen index as described leading indicators, and by described time difference value in the 4th span and described K- L quantity of information k_lLess than the selected index of the 4th threshold value as described coincidence indicator.

Device the most according to claim 8 or claim 9, it is characterised in that described second processing module includes:

5th sub-processing module, for carrying out symmetrical change process respectively, to obtain to described leading indicators and described coincidence indicator Take leading indicators symmetry rate of change C_w,i(t) and coincidence indicator symmetry rate of change C_z,i(t), described 5th sub-processing module includes:

4th sub-computing module, for described leading indicators being carried out symmetrical change process by equation below, described to obtain Leading indicators symmetry rate of change C_w,i(t):

Wherein,Be i-th (i=1,2 ..., k_w) individual leading indicators, t=2, 3 ..., n is moon number, k_wNumber for leading indicators；

5th sub-computing module, for described coincidence indicator being carried out symmetrical change process by equation below, consistent to obtain Index symmetry rate of change C_z,i(t):

Wherein,Be i-th (i=1,2 ..., k_z) individual coincidence indicator, t=2, 3 ..., n, k_zIt it is the number of coincidence indicator；

6th sub-processing module, for described leading indicators symmetry rate of change C_w,i(t) and described coincidence indicator symmetry rate of change C_z,iT () is standardized processing and after trend adjustment, the result that obtains carries out composite calulation, refers to obtaining described leading synthesis Number and coincident composite Index.

11. devices according to claim 10, it is characterised in that described 6th sub-processing module includes:

6th sub-computing module, for obtaining normalization factor A by equation below_w,iAnd A_z,i: T=2,3 ..., n；

7th sub-processing module, is used for using described normalization factor A_w,iAnd A_z,iRespectively by described leading indicators symmetry rate of change C_w,i(t) and described coincidence indicator symmetry rate of change C_z,iT () is standardized processing, to obtain standardization rate of change S_w,i(t) and S_z,i(t), wherein,

$S_{w,i}\left(t\right)=\frac{C_{w,i}\left(t\right)}{A_{w,i}},S_{z,i}\left(t\right)=\frac{C_{z,i}\left(t\right)}{A_{z,i}},t=2,3,...,n;$

8th sub-processing module, for described standardization rate of change S_w,i(t) and S_z,i(t) be averaged rate of change process, with Obtain standardization average rate of change V of described leading indicators_wStandardization average rate of change V of (t) and described coincidence indicator_z(t)；

7th sub-computing module, for standardization average rate of change V according to described leading indicators_w(t) and described coincidence indicator Standardization average rate of change V_zT () carries out composite calulation, to obtain described leading composite index number I_w(t) and coincident composite Index I_z (t), wherein,And I_w(1)=100, I_z(1)= 100。

12. devices according to claim 11, it is characterised in that described 8th sub-processing module includes:

9th sub-processing module, is used for by equation below respectively by the standardization rate of change S of described leading indicators_w,i(t) and institute State the standardization rate of change S of coincidence indicator_z,i(t) be averaged rate of change process, to obtain the mean change of described leading indicators Rate R_wAverage rate of change R of (t) and described coincidence indicator_z(t):

Wherein, λ_w,iAnd λ_z,iIt is leading indicators and coincidence indicator respectively The weight of i-th index；

8th sub-computing module, for obtaining criterion factor F by equation below_w:

$F_w=\&lsqb;\underset{t=2}{\overset{n}{\&Sigma;}}\left|R_w\left(t\right)\right|/(n-1)\&rsqb;/\&lsqb;\underset{t=2}{\overset{n}{\&Sigma;}}\left|R_z\left(t\right)\right|/(n-1)\&rsqb;;$

9th sub-computing module, for according to described criterion factor F_wIt is standardized the average rate of change to process, to obtain Standardization average rate of change V of described leading indicators_wStandardization average rate of change V of (t) and described coincidence indicator_z(t), wherein, V_w(t)=R_w(t)/F_w, V_z(t)=R_z(t)。