CN106845693A - A kind of method and system for predicting random process variation tendency transition point - Google Patents
A kind of method and system for predicting random process variation tendency transition point Download PDFInfo
- Publication number
- CN106845693A CN106845693A CN201611256448.5A CN201611256448A CN106845693A CN 106845693 A CN106845693 A CN 106845693A CN 201611256448 A CN201611256448 A CN 201611256448A CN 106845693 A CN106845693 A CN 106845693A
- Authority
- CN
- China
- Prior art keywords
- sliding window
- diffusion index
- time
- time interval
- numerical value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
- G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Technology Law (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a kind of method and system for predicting random process variation tendency transition point, the method is：The numerical value of change at random is gathered, time series is formed；The time interval of numerical value is chosen, standard deviation is obtained after determining the data variation amount in time interval；Standard deviation and time interval are fitted, diffusion index is obtained；With time interval scope as abscissa, the diffusion index of time series is as ordinate, draw relation curve, the length of sliding window is determined with the trough of this curve, the diffusion index under sliding window is calculated using short sliding window, this random process occurrence tendency near sliding window finish time is then indicated when diffusion index is significantly less than 0.5 and is changed；Forecasting Methodology of the present invention is simple, it is easy to operate, and the present invention is, according to support, diffusion index to be obtained by the fitting of function with real data, and with scientific and real-time, and the degree of accuracy predicted is high.
Description
Technical field
The invention belongs to data processing field, and in particular to a kind of method of prediction random process variation tendency transition point and
System.
Background technology
Stock market is the important component in financial market, and closely related with the national economic development, stock is used as one kind
The representative of fictitious capital, easily can finance in stock market.On the surface, stock market is a lack of under ordinary meaning
It is regular, it appears that its variation is difficult to predict, but many economist's energies throughout one's life, create various Mathematical Modelings
To simplify stock market, flow direction and its regularity of capital are sounded out.
Research of the economist to the analyzing and predicting method of the various asset prices with stock price as representative, can be with
It is divided into fundamental analysis and the major class of technical Analysis two.Fundamental analysis is the Fundamentals by studying influence security price, it is determined that card
The inherent value of certificate, finds out by the security of market mispricing, buys in and holds for a long time, until the market price of security is returned to
Untill its inherent value；Technical Analysis are then the historical datas according to stock market information, are sought by chart, technical indicator etc.
The rule of stock price change best being bought in a little and selling a little to find.
With going deep into for mathematical theory research and developing rapidly that various data analysis tools are developed, technical Analysis are gradually deep
Enter the popular feeling, people analyze financial time series with a variety of Method and kit fors, make various Financial Time Series Forecastings
Model, especially the time series of stock price have huge attraction to dopester always, be various Forecasting Methodologies should
Popular domain,
Share certificate transaction analysis software in the very powerful freenet of many functions for occurring in the market, for example together
The softwares such as Hua Shun, sensible letter, DZH.Their basic function is real-time announcement and the K line chart technical Analysis of information, including each
Plant technical indicator, quotation information, information etc..With the development of security analysis technology and software engineering, with regard to equity investment side
For method opinion, stock tickers evolve many functions：Technical Analysis, fundamental analysis, EVOLUTION ANALYSIS, and information are collected, intelligence
Select stocks, select stocks automatically, link consignment trade etc..
The essence of stock tickers is, by the statistics to historical data, to set up analysis model and be presented to user on the market, is used
Family need to fall according to the liter in conventional micro-judgment stock later stage.The method for predicting stock price tendency by the stock tickers does not have
Prediction Parameters as support, with not scientific；And rely primarily on the micro-judgment price trend of individual subscriber, it is difficult to adapt to
Fast changing stock market.
Therefore, in order to solve the not scientific of existing Financial Time Series Forecasting model, a science is needed badly accurate
The system and method for predicted time sequence transitions point.
The content of the invention
It is random the invention provides one kind prediction in order to solve the not scientific of existing Financial Time Series Forecasting model
The method and system of change in process trend reverse point.The technical problem to be solved in the present invention is achieved through the following technical solutions：
A kind of system for predicting random process variation tendency turning point, including：
Data capture unit, for obtaining the numerical value of change at random, and forms time series；
First determining unit, the time interval for determining the numerical value；
First processing units, for calculating the data variation amount under time series in multiple time intervals, and to institute
State time series, the time interval and the data variation amount to be processed, obtain diffusion index；
Image generation unit, the diffusion index under multiple different time interval scopes is in smoothing junction, generate one and put down
Sliding curve；
Second determining unit, for determining time window length according to the curve, and chooses in the time series
The time window length is used as sliding window；
Data on each described sliding window, are processed by second processing unit, obtain the expansion of the sliding window
Dissipate index；
Data analysis unit, for being analyzed to the diffusion index uniquely determined on sliding window each described,
Also, when the diffusion index of the sliding window is significantly less than 0.5, is sent to pre-alarm unit and instructed；
Pre-alarm unit, for receiving the instruction of data analysis unit and being alarmed.
A kind of method for predicting random process variation tendency transition point, becomes using a kind of above-mentioned prediction random process change
The system of gesture turning point, comprises the following steps：
The numerical value of step one, search or collection change at random, time series, time series such as formula (1) institute are formed by numerical value
Show：
Y (t) (t=1,2 ..., N) (1)
Step 2, the time interval for choosing the numerical value, are taken after determining the data variation amount in the time interval
The standard deviation of value changes；
Shown in the computational methods of data variation amount such as formula (2)：
YΔt(t)=Y (t+ Δs t)-Y (t) (2)
Wherein, Δ t is time interval, and Δ t=1,2 ..., n；
Shown in the computational methods such as formula (3) of the standard deviation of value change：
Wherein, n is the scope of time interval；
Step 3, the standard deviation and time interval are fitted, obtain diffusion index, the computational methods such as formula of diffusion index
(4) shown in：
σΔt~Δ tD (4)
Wherein, power exponent D is diffusion index；
Step 4, by under multiple time interval scopes diffusion index connection, obtain a smooth curve；
Step 5, time window length is determined according to the curve, and the time window is chosen in the time series
Mouth length is used as sliding window；
Step 6, on each described sliding window, determine the standard deviation of data variation in each described time interval, will
Standard deviation is fitted with time interval, obtains the diffusion index of the sliding window；
Wherein, shown in the computational methods of standard deviation such as formula (3)；The computational methods such as formula of the sliding window diffusion index
(4) shown in；
Step 7, the movement with sliding window, when the diffusion index of sliding window drops to less than 0.5 by more than 0.5
When, then it represents that in the vicinity of sliding window finish time of the diffusion index less than 0.5, numerical value is by the transformation of occurrence tendency.
The method of above-mentioned a kind of prediction random process variation tendency transition point, also including step 8：When the sliding window
When the diffusion index of mouth is significantly less than 0.5, data analysis unit sends to pre-alarm unit and instructs, and pre-alarm unit receives finger
Alarmed after order.
Compared with prior art, beneficial effects of the present invention：
1. the present invention with time interval scope as abscissa, the diffusion index of time series as ordinate, draw out one
Relation curve, the length of sliding window is determined with the trough of this curve, is calculated under sliding window using short sliding window
Diffusion index, this random process near sliding window finish time is then indicated when diffusion index is significantly less than 0.5
Occurrence tendency changes, and Forecasting Methodology of the present invention is simple, it is easy to operate；
2. the present invention is, according to support, diffusion index to be obtained by the fitting of function with real data, with scientific and real
Shi Xing, and the degree of accuracy predicted is high.
Brief description of the drawings
Fig. 1 is structural representation of the invention；
Fig. 2 is flow chart of the invention；
Fig. 3 is the graph of relation of Hu-Shen 300 index；
Fig. 4 is the stock price and time series comparison diagram of Hu-Shen 300 index.
Specific embodiment
Further detailed description is done to the present invention with reference to specific embodiment, but embodiments of the present invention are not limited to
This.
Embodiment 1：
Reference picture 1, a kind of system for predicting random process variation tendency turning point, including：
Data capture unit, for obtaining the numerical value of change at random, and forms time series；
First determining unit, the time interval for determining the numerical value；
First processing units, for calculating the data variation amount under time series in multiple time intervals, and to institute
State time series, the time interval and the data variation amount to be processed, obtain diffusion index；
Image generation unit, the diffusion index under multiple different time interval scopes is in smoothing junction, generate one and put down
Sliding curve；
Second determining unit, for determining time window length according to the curve, and chooses in the time series
The time window length is used as sliding window；
Data on each described sliding window, are processed by second processing unit, obtain the expansion of the sliding window
Dissipate index；
Data analysis unit, for being analyzed to the diffusion index uniquely determined on sliding window each described,
Also, when the diffusion index of the sliding window is significantly less than 0.5, is sent to pre-alarm unit and instructed；
Pre-alarm unit, for receiving the instruction of data analysis unit and being alarmed.
A kind of reference picture 2, method for predicting random process variation tendency transition point, random mistake is predicted using above-mentioned one kind
The system of journey variation tendency turning point, comprises the following steps：
The numerical value of step one, search or collection change at random, time series, time series such as formula (1) institute are formed by numerical value
Show：
Y (t) (t=1,2 ..., N) (1)
Step 2, the time interval for choosing the numerical value, are taken after determining the data variation amount in the time interval
The standard deviation of value changes；
Shown in the computational methods of data variation amount such as formula (2)：
YΔt(t)=Y (t+ Δs t)-Y (t) (2)
Wherein, Δ t is time interval, and Δ t=1,2 ..., n；T is time interval；
Shown in the computational methods such as formula (3) of the standard deviation of value change：
The scope of time interval is n.
Step 3, the standard deviation and time interval are fitted, obtain diffusion index, the computational methods such as formula of diffusion index
(4) shown in：
σΔt~Δ tD (4)
Wherein, power exponent D is diffusion index；
Step 4, by under multiple time interval scopes diffusion index connection, obtain a smooth curve；
Step 5, time window length is determined according to the curve, and the time window is chosen in the time series
Mouth length is used as sliding window；
Step 6, on each described sliding window, determine the standard deviation of data variation in each described time interval, will
Standard deviation is fitted with time interval, obtains the diffusion index of the sliding window；
Wherein, shown in the computational methods of standard deviation such as formula (3)；The computational methods such as formula of the sliding window diffusion index
(4) shown in；
Step 7, the movement with sliding window, when the diffusion index of sliding window drops to less than 0.5 by more than 0.5
When, then it represents that in the vicinity of sliding window finish time of the diffusion index less than 0.5, numerical value is by the transformation of occurrence tendency；
Step 8：When the diffusion index of the sliding window is significantly less than 0.5, data analysis unit is to pre-alarm unit
Instruction is sent, pre-alarm unit is alarmed after receiving instruction.
The present invention is abscissa by time interval scope, the diffusion index of time series is ordinate, draws out one
Relation curve, the length of sliding window is determined with the trough of this curve, is calculated under sliding window using short sliding window
Diffusion index, this random process near sliding window finish time is then indicated when diffusion index is significantly less than 0.5
Occurrence tendency changes, and Forecasting Methodology of the present invention is simple, it is easy to operate；The present invention is according to support, by letter with real data
Several fittings obtain diffusion index, and with scientific and real-time, and the degree of accuracy predicted is high.
Embodiment 2：
The present embodiment is by taking Hu-Shen 300 index as an example, and narration in detail is of the invention：
Reference picture 2- Fig. 4, the prediction of stock index variation tendency is achieved in that Hu-Shen 300 index is per minute
Price is designated as Y (t), and its value is represented with Y (t) (t=1,2 ..., N)；
YΔtT variable quantity that () occurs for value Y (t) of t after elapsed time interval of delta t, uses YΔt(t)=Y (t+ Δs
T)-Y (t) is represented；
Take YΔt(t) change standard deviation be：
By standard deviation sigmaΔtWith the fitting formula σ of interval of delta tΔt~Δ tD, power exponent D is obtained, power exponent D is called that diffusion refers to
Number；
The relation of diffusion index D and interval n is calculated with above-mentioned formula, is that abscissa, diffusion refer to by interval n
Number D is ordinate, draws out a relation curve, as a result such as accompanying drawing 3；
Wherein, from time t, each time interval has a variable quantity, YΔt(t)=Y (t+ Δs t)-Y (t)；Choosing
After interval of delta t of fixing time, from different moment t, there are different variable quantity YΔt.These variable quantities have a variances sigmaΔt；
To time interval Δ t and variances sigmaΔtIt is fitted, diffusion index D can be obtained；The value orientation of time interval Δ t is 1,
2 ..., n, its maximum are n.When n very littles, we only considered very short time interval, and diffusion index only reflects
The characteristic of the change at random in very short time；When n is very big, diffusion index will reflect prolonged tendency change,
So, we will look at the change of diffusion index interval n over time.
It may be noted that this relation curve shows a peak first, increase again after a trough, in first ripple
The right side of paddy selects an abscissa as a length for small time window, it may be noted that the choosing of small time window length
Certain free degree is selected, it on the right side of paddy, but must should be close to this paddy, make its ordinate D less big, remember it
Abscissa value be W.
As shown in Figure 3, we determined that the length W=10000 of small time window, chooses in existing time series
Length is 10000 sliding window.Standard deviation sigma is calculated on each sliding windowΔtWith the relation curve of interval of delta t, pass through
It is fitted to formula σΔt~Δ tDPower exponent D is obtained, we end up the value of this power exponent D as window the value at moment, draw
The value of power exponent D change curve over time.Then stock price time series is subject to the time series of power exponent D values
Contrast, as a result in fig. 4；
When power exponent D values are less than after 0.45, stock price changes in the neighbouring of current window finish time, when becoming
When gesture changes, the influence that tendency changes to diffusion index disappears, so as to cause diffusion index D to decline.
The present embodiment is innovatively：Calculate key index using short time window W, length of window W by power exponent D with
The trough of the relation curve between the value n of largest interval determines that the situation that D is significantly less than 0.5 indicates trend transition point.
It should be noted that the present invention is not limited in predicting the trend transition point of stock, as long as a variation tendency
The Random time sequence for changing over time, the trend transformation of the time series can be predicted using the method for the present invention
Point.
Above content is to combine specific preferred embodiment further description made for the present invention, it is impossible to assert
Specific implementation of the invention is confined to these explanations.For general technical staff of the technical field of the invention,
On the premise of not departing from present inventive concept, some simple deduction or replace can also be made, should be all considered as belonging to of the invention
Protection domain.
Claims (3)
1. it is a kind of predict random process variation tendency turning point system, it is characterised in that including：
Data capture unit, for obtaining the numerical value of change at random, and forms time series；
First determining unit, the time interval for determining the numerical value；
First processing units, for calculating the data variation amount under time series in multiple time intervals, and to it is described when
Between sequence, the time interval and the data variation amount processed, obtain diffusion index；
Image generation unit, the diffusion index under multiple different time interval scopes is in smoothing junction, and generation one is smooth
Curve；
Second determining unit, for determining time window length according to the curve, and chooses described in the time series
Time window length is used as sliding window；
Data on each described sliding window, are processed by second processing unit, and the diffusion for obtaining the sliding window refers to
Number；
Data analysis unit, for being analyzed to the diffusion index uniquely determined on sliding window each described, also,
When the diffusion index of the sliding window is significantly less than 0.5, is sent to pre-alarm unit and instructed；
Pre-alarm unit, for receiving the instruction of data analysis unit and being alarmed.
2. a kind of method for predicting random process variation tendency transition point, random mistake is predicted using the one kind described in claim 1
The system of journey variation tendency turning point, comprises the following steps：
The numerical value of step one, search or collection change at random, forms time series, shown in time series such as formula (1) by numerical value：
Y (t) (t=1,2 ..., N) (1)
Step 2, the time interval for choosing the numerical value, value change is obtained after determining the data variation amount in the time interval
The standard deviation of change；
Shown in the computational methods of data variation amount such as formula (2)：
YΔt(t)=Y (t+ Δs t)-Y (t) (2)
Wherein, Δ t is time interval, and Δ t=1,2 ..., n；
Shown in the computational methods such as formula (3) of the standard deviation of value change：
Wherein, n is the scope of time interval；
Step 3, the standard deviation and time interval are fitted, obtain diffusion index, the computational methods such as formula (4) of diffusion index
It is shown：
σΔt~Δ tD (4)
Wherein, power exponent D is diffusion index；
Step 4, by under multiple intervals diffusion index connection, obtain a smooth curve；
Step 5, time window length is determined according to the curve, and it is long that the time window is chosen in the time series
Degree is used as sliding window；
Step 6, on each described sliding window, the standard deviation of data variation in each described time interval is determined, by standard
Difference and time interval fitting, obtain the diffusion index of the sliding window；
Wherein, shown in the computational methods of standard deviation such as formula (3)；Computational methods such as formula (4) institute of the sliding window diffusion index
Show；
Step 7, the movement with sliding window, when the diffusion index of sliding window more than 0.5 by dropping to less than 0.5, then
Represent that, in the vicinity of sliding window finish time of the diffusion index less than 0.5, numerical value is by the transformation of occurrence tendency.
3. it is according to claim 2 prediction random process variation tendency transition point method, it is characterised in that also including step
Rapid eight：When the diffusion index of the sliding window is significantly less than 0.5, data analysis unit sends to pre-alarm unit and instructs,
Pre-alarm unit is alarmed after receiving instruction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611256448.5A CN106845693A (en) | 2016-12-30 | 2016-12-30 | A kind of method and system for predicting random process variation tendency transition point |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611256448.5A CN106845693A (en) | 2016-12-30 | 2016-12-30 | A kind of method and system for predicting random process variation tendency transition point |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106845693A true CN106845693A (en) | 2017-06-13 |
Family
ID=59113541
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611256448.5A Pending CN106845693A (en) | 2016-12-30 | 2016-12-30 | A kind of method and system for predicting random process variation tendency transition point |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106845693A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108985632A (en) * | 2018-07-16 | 2018-12-11 | 国网上海市电力公司 | A kind of electricity consumption data abnormality detection model based on isolated forest algorithm |
-
2016
- 2016-12-30 CN CN201611256448.5A patent/CN106845693A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108985632A (en) * | 2018-07-16 | 2018-12-11 | 国网上海市电力公司 | A kind of electricity consumption data abnormality detection model based on isolated forest algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Aggarwal et al. | A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices | |
| Hansun | A new approach of moving average method in time series analysis | |
| Wu et al. | A prediction method using the grey model GMC (1, n) combined with the grey relational analysis: a case study on Internet access population forecast | |
| Chai et al. | Conditional density forecast of electricity price based on ensemble ELM and logistic EMOS | |
| Lavielle et al. | Adaptive detection of multiple change-points in asset price volatility | |
| Qin et al. | Simulating and Predicting of Hydrological Time Series Based on TensorFlow Deep Learning. | |
| CN105787582A (en) | Stock risk prediction method and apparatus | |
| CN106095895B (en) | Information pushing method and device | |
| Zuo et al. | Up/down analysis of stock index by using Bayesian network | |
| CN109727446A (en) | A kind of identification and processing method of electricity consumption data exceptional value | |
| Bijak et al. | Modelling LGD for unsecured retail loans using Bayesian methods | |
| CN110245782A (en) | Prediction model training method, device and equipment | |
| Yao et al. | Recurrence plots analysis of the CNY exchange markets based on phase space reconstruction | |
| CN111738856A (en) | Stock public opinion investment decision analysis method and device | |
| Jabeen et al. | An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning | |
| Winata et al. | Predicting stock market prices using time series sarima | |
| CN109947946A (en) | A kind of prediction article propagates the method and device of temperature | |
| CN106845693A (en) | A kind of method and system for predicting random process variation tendency transition point | |
| CN111860998A (en) | Stock transaction data prediction method, apparatus, electronic device and medium | |
| CN113421154B (en) | Credit risk assessment method and system based on control chart | |
| Bai et al. | Mercury: A Deep Reinforcement Learning-Based Investment Portfolio Strategy for Risk-Return Balance | |
| Bertrand et al. | Overfitting of Hurst estimators for multifractional Brownian motion: A fitting test advocating simple models | |
| JP7451935B2 (en) | Prediction program, prediction method and prediction device | |
| Vaidya et al. | Decision support system for the stock market using data analytics and artificial intelligence | |
| Yang et al. | Value at risk estimation under stochastic volatility models using adaptive PMCMC methods |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20170613 |