CN112288158A - Service data prediction method and related device - Google Patents
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Abstract
The application discloses a service data prediction method, which comprises the following steps: the method comprises the steps of constructing a prediction model by taking service data as a time sequence according to a time sequence decomposition algorithm to obtain a time sequence prediction model; correcting the time sequence prediction model according to the service data of the last time period to obtain a target prediction model; and predicting the service data in the next time period according to the target prediction model to obtain a service data prediction result. The time prediction model is constructed in the angle of the time sequence and is corrected to obtain the target prediction model, and the service data in the next time period is predicted, so that the accuracy of service data prediction is improved, and the prediction error is reduced. The application also discloses a service data prediction device, a server and a computer readable storage medium, which have the beneficial effects.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a business data prediction method, a business data prediction apparatus, a server, and a computer-readable storage medium.
Background
With the continuous development of information technology, more and more data processing technologies are applied to the field of statistical prediction. Statistical prediction is a theory and technology for making conjectures and estimates of the development trend of things and the quantitative performance of the things in future periods. The statistical prediction is based on the development rules of natural phenomena and social phenomena, takes sufficient statistical data and latest information as the basis, takes a statistical method and a mathematical method as means, is matched with a proper mathematical model, finds out the regularity of the change of the quantity of the object through reasoning and calculation, and quantitatively deduces the future situation of the object, namely points out the possible range under certain probability guarantee from various quantity expressions which may appear in the future of the object. Therefore, when the data processing technology is applied to the field of data prediction, the efficiency and the accuracy of data prediction are greatly improved.
In the related art, the operation of predicting the time-related business data in an enterprise mainly adopts the business data in the previous time period for prediction, and corrects the prediction result according to the experience of a technician so as to obtain the final prediction result. However, due to the addition of artificial subjective experience, the prediction result lacks a support basis, so that the reliability of the prediction result is reduced, errors are increased, and the accuracy of prediction is reduced.
Therefore, how to improve the accuracy of the business data prediction and reduce the prediction error is a key issue of attention of those skilled in the art.
Disclosure of Invention
The application aims to provide a service data prediction method, a service data prediction device, a server and a computer readable storage medium, a time prediction model is built in a time sequence angle and is corrected to obtain a target prediction model, service data in the next time period are predicted, accuracy of service data prediction is improved, and prediction errors are reduced.
In order to solve the above technical problem, the present application provides a service data prediction method, including:
the method comprises the steps of constructing a prediction model by taking service data as a time sequence according to a time sequence decomposition algorithm to obtain a time sequence prediction model;
correcting the time sequence prediction model according to the service data of the last time period to obtain a target prediction model;
and predicting the service data in the next time period according to the target prediction model to obtain a service data prediction result.
Optionally, the constructing a prediction model by using the service data as a time sequence according to a time sequence decomposition algorithm to obtain a time sequence prediction model includes:
decomposing the service data according to an STL algorithm to obtain a trend component, a seasonal component and a residual error component;
performing function construction according to the trend component, the seasonal component and the residual error component to obtain a horizontal function, a trend function and a seasonal function;
determining the prediction function from the level function, the trend function, and the seasonal function;
and taking the level function, the trend function, the seasonal function and the prediction function as the time sequence prediction model.
Optionally, the modifying the time sequence prediction model according to the service data of the last time period to obtain a target prediction model includes:
performing prediction processing according to a prediction model constructed according to the service data of the previous time period to obtain prediction data of the current time period;
calculating the deviation degree according to actual data corresponding to the current time period in the service data and the predicted data of the current time period to obtain the deviation degree;
and correcting the time sequence prediction model according to the deviation degree to obtain the target prediction model.
Optionally, the method further includes:
and when the service data of a new time period is received, updating the target prediction model according to the service data of the new time period to obtain a new target prediction model.
The present application further provides a service data prediction apparatus, including:
the model construction module is used for constructing a prediction model by taking the service data as a time sequence according to a time sequence decomposition algorithm to obtain a time sequence prediction model;
the model correction module is used for correcting the time sequence prediction model according to the service data of the last time period to obtain a target prediction model;
and the data prediction module is used for predicting the service data in the next time period according to the target prediction model to obtain a service data prediction result.
Optionally, the model building module includes:
the decomposition unit is used for decomposing the service data according to an STL algorithm to obtain a trend component, a seasonal component and a residual error component;
the function construction unit is used for performing function construction according to the trend component, the seasonal component and the residual error component to obtain a horizontal function, a trend function and a seasonal function;
a prediction function acquisition unit configured to determine the prediction function from the level function, the trend function, and the seasonal function;
a prediction model acquisition unit configured to take the level function, the trend function, the seasonal function, and the prediction function as the time series prediction model.
Optionally, the model modification module includes:
the last time period prediction unit is used for performing prediction processing according to a prediction model constructed by the last time period service data to obtain prediction data of the current time period;
the deviation calculation unit is used for calculating the deviation according to actual data corresponding to the current time period in the service data and the predicted data of the current time period to obtain the deviation;
and the model correction unit is used for correcting the time sequence prediction model according to the deviation degree to obtain the target prediction model.
Optionally, the method further includes:
and the model updating module is used for updating the target prediction model according to the service data of the new time period when the service data of the new time period is received, so as to obtain a new target prediction model.
The present application further provides a server, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the traffic data prediction method as described above when executing the computer program.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, realizes the steps of the traffic data prediction method as described above.
The application provides a business data prediction method, which comprises the following steps: the method comprises the steps of constructing a prediction model by taking service data as a time sequence according to a time sequence decomposition algorithm to obtain a time sequence prediction model; correcting the time sequence prediction model according to the service data of the last time period to obtain a target prediction model; and predicting the service data in the next time period according to the target prediction model to obtain a service data prediction result.
The method comprises the steps of constructing a prediction model by taking service data as a time sequence according to a time sequence decomposition algorithm to obtain a time sequence prediction model, and then correcting the time sequence prediction model according to the service data of the previous time period to obtain a target prediction model; and finally, predicting the service data in the next time period according to the target prediction model to obtain a service data prediction result, so that the service data prediction is realized.
The present application further provides a service data prediction apparatus, a server, and a computer-readable storage medium, which have the above beneficial effects and are not described herein again.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a service data prediction method according to an embodiment of the present application;
FIG. 2 is a time series exploded view of an embodiment of the present application;
fig. 3 is a schematic structural diagram of a service data prediction apparatus according to an embodiment of the present application.
Detailed Description
The core of the application is to provide a service data prediction method, a service data prediction device, a server and a computer readable storage medium, a time prediction model is constructed in a time sequence angle, a target prediction model is obtained after correction, service data in the next time period are predicted, the accuracy of service data prediction is improved, and prediction errors are reduced.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the related art, the operation of predicting the time-related business data in an enterprise mainly adopts the business data in the previous time period for prediction, and corrects the prediction result according to the experience of a technician so as to obtain the final prediction result. However, due to the addition of artificial subjective experience, the prediction result lacks a support basis, so that the reliability of the prediction result is reduced, errors are increased, and the accuracy of prediction is reduced.
Therefore, the application provides a business data prediction method, a time sequence prediction model is obtained by constructing a prediction model by taking business data as a time sequence according to a time sequence decomposition algorithm, and then the time sequence prediction model is corrected according to the business data of the previous time period to obtain a target prediction model; and finally, predicting the service data in the next time period according to the target prediction model to obtain a service data prediction result, so that the service data prediction is realized.
The following describes a business data prediction method provided by the present application, by way of an embodiment.
Referring to fig. 1, fig. 1 is a flowchart illustrating a business data prediction method according to an embodiment of the present disclosure.
In this embodiment, the method may include:
s101, constructing a prediction model by taking the service data as a time sequence according to a time sequence decomposition algorithm to obtain a time sequence prediction model;
the method comprises the steps of constructing a prediction model by taking service data as a time sequence according to a time sequence decomposition algorithm to obtain a time sequence prediction model. Namely, the service data is used as time sequence data in a preset time period, and then a time sequence decomposition algorithm is adopted to construct a prediction model for the time sequence data to obtain a time sequence prediction model.
The preset time period can be selected according to different application conditions, and can be a day period, a month period or a year period.
The time sequence decomposition algorithm may adopt any one of the time sequence decomposition algorithms provided by the prior art, and may also adopt an STL time sequence decomposition algorithm. It is to be noted that the STL timing decomposition algorithm used in this step is not unique, and is not specifically limited herein.
Further, in order to improve the accuracy of the model, the method may include:
step 1, decomposing service data according to an STL algorithm to obtain a trend component, a seasonal component and a residual error component;
and 4, taking the horizontal function, the trend function, the seasonal function and the prediction function as a time sequence prediction model.
It can be seen that the alternative is mainly how to construct a time sequence prediction model for explanation. In the alternative, firstly, service data is decomposed according to an STL (time series-Trend-based decomposition on Loess) algorithm to obtain a Trend component, a Seasonal component and a residual component; then, performing function construction according to the trend component, the seasonal component and the residual error component to obtain a horizontal function, a trend function and a seasonal function; then, determining a prediction function according to the horizontal function, the trend function and the seasonal function; and finally, taking the horizontal function, the trend function, the seasonal function and the prediction function as a time sequence prediction model.
S102, correcting the time sequence prediction model according to the service data of the last time period to obtain a target prediction model;
on the basis of S101, this step aims to modify the time-series prediction model according to the service data of the previous time period, so as to obtain a target prediction model. Therefore, in the alternative scheme, the time sequence prediction model obtained in the previous step is mainly corrected, so that the error of the time sequence prediction model is reduced, and the accuracy of the prediction process is further improved.
The method mainly comprises the steps of predicting according to the service data of the last time period, carrying out deviation calculation on the service data and actual service data to obtain a deviation degree, and correcting the time sequence prediction model by adopting the deviation degree. The last time period may be the last day, the last month or the last year.
Further, the step may include:
step 1, carrying out prediction processing according to a prediction model constructed by service data of a previous time period to obtain prediction data of the current time period;
and 3, correcting the time sequence prediction model according to the deviation degree to obtain a target prediction model.
It can be seen that the present alternative is mainly illustrative of how the correction process is performed. In the alternative, prediction processing is performed according to a prediction model constructed according to the service data of the previous time period to obtain prediction data of the current time period, namely, prediction is performed on the service data of the known time period. And then, calculating the deviation degree according to actual data corresponding to the current time period in the service data and the predicted data of the current time period to obtain the deviation degree, and calculating the deviation degree between the known service data and the predicted service data. And finally, correcting the time sequence prediction model according to the deviation degree to obtain a target prediction model, so that errors in the time sequence prediction model are avoided.
S103, predicting the service data in the next time period according to the target prediction model to obtain a service data prediction result.
On the basis of S102, the step is to predict the service data in the next time period according to the obtained target prediction model, so as to obtain a service data prediction result. That is, the target prediction model is used for predicting the business data.
For example, the business data in this embodiment may be tax data, which is used to predict tax income data in the next month.
On the basis of obtaining the target prediction model, any prediction execution manner provided in the prior art may be adopted, which is not specifically limited herein.
In summary, in the embodiment, the service data is used as a time sequence to construct the prediction model according to the time sequence decomposition algorithm, so as to obtain the time sequence prediction model, and then the time sequence prediction model is modified according to the service data of the previous time period, so as to obtain the target prediction model; and finally, predicting the service data in the next time period according to the target prediction model to obtain a service data prediction result, so that the service data prediction is realized.
The service data prediction method provided by the present application is further described below by another specific embodiment.
In this embodiment, the tax input data is mainly predicted. Monthly revenue was first analyzed as a time series by itself, and not considering each factor separately. Meanwhile, tax data is used as a time sequence, has annual periodicity and overall trend, and is suitable for analysis after STL decomposition.
The process of this embodiment may include:
step 1, constructing a prediction model by taking service data as a time sequence according to a time sequence decomposition algorithm to obtain a time sequence prediction model;
and 3, predicting the service data in the next time period according to the target prediction model to obtain a service data prediction result.
The STL is a common algorithm in time series decomposition, and for a time series data set, may be decomposed into three parts: trend (Trend), seasonality (Seasonal) and Residual (Residual).
Referring to fig. 2, fig. 2 is a schematic time-series diagram according to an embodiment of the present disclosure.
In fig. 2, the first row is the monthly real amount of the incremental tax amount in the Shanghai bonded area after 2017, and after STL decomposition, the second row is a trend variable reflecting the overall change trend; the third row is a seasonal variable, reflecting the change in the regularity of the annual cycle; the fourth row is the residual variable.
Based on the decomposition result, the prediction models corresponding to different data can be obtained. The model is composed of a prediction function and a cubic smoothing function, including a level function LtTrend function btSeasonal function StAnd smoothing parameters α, β, and γ.
Level function:
Lt＝α(yt-St-s)+(1-α)(Lt-1+bt-1)；
trend function:
bt＝β(Lt-Lt-1)+(1-β)(bt-1)；
seasonal function:
St＝γ(yt-Lt)+(1-γ)(St-s)；
the prediction function:
Ft+k＝Lt+kbt+St-k-s。
wherein s is the length of seasonal cycle, alpha is more than or equal to 0 and less than or equal to 1, beta is more than or equal to 0 and less than or equal to 1, and gamma is more than or equal to 0 and less than or equal to 1. The level function is a weighted average between the seasonally adjusted observations and the non-seasonal prediction at time t, the level function display is a weighted average of the observations and the predicted single step values within the sample, and the trend function display is a weighted average of the predicted trends at time t based on L (t) -L (t-1) and the previous predicted trend b (t-1). The seasonal function is a weighted average between the current seasonal index and the seasonal index for the same season in the last year. And obtaining a prediction function by using the three functions.
The latest month tax amount is predicted by using the tax amount real amount of each historical month and the established model, and the prediction mode is based on historical data completely. In this embodiment, in order to reduce the error, part of the data in the latest time may be introduced for correction. The last month's invoice data may be used. For example, to predict the value-added tax income of 9 months in 2020, a model is established from 1 month in 2017 to 8 months in 2020, and the value-added tax income of 9 months in 2020 can be predicted to be a. And the income of the value-added tax in 9 month in 2020 is influenced by the invoice data in 8 month in 2020, a time prediction model is also established by using the special invoice value-added tax amount from 1 month in 2017 to 7 month in 2020, the invoice data value-added tax amount b in 8 month in 2020 is predicted, and then the value-added tax amount b is compared with the actual invoice data value-added tax amount b0 in 8 month in 2020, for example, b is 10% higher than b0, which shows that the actual economic data in 8 month is lower than the data predicted theoretically, so that the income of the actual value-added tax in 9 month is probably lower than the income of the predicted value-added tax. Therefore, the value-added tax income prediction data of 9 months is corrected downwards, and a more accurate result is achieved.
In summary, the embodiment constructs a comprehensive time series model for predicting monthly revenue based on historical revenue data and monthly invoice value-added tax amount before the month. And when the next month is reached, the data of the month is added to the model for rolling updating, so that continuous updating and optimization are realized.
As can be seen, in the embodiment, the service data is used as a time sequence to construct the prediction model according to the time sequence decomposition algorithm, so as to obtain the time sequence prediction model, and then the time sequence prediction model is modified according to the service data of the previous time period, so as to obtain the target prediction model; and finally, predicting the service data in the next time period according to the target prediction model to obtain a service data prediction result, so that the service data prediction is realized.
In the following, the service data prediction apparatus provided in the embodiment of the present application is introduced, and the service data prediction apparatus described below and the service data prediction method described above may be referred to correspondingly.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a service data prediction apparatus according to an embodiment of the present application.
In this embodiment, the method may include:
the model construction module 100 is configured to construct a prediction model by using the service data as a time sequence according to a time sequence decomposition algorithm, so as to obtain a time sequence prediction model;
the model modification module 200 is configured to modify the time-series prediction model according to the service data of the previous time period to obtain a target prediction model;
and the data prediction module 300 is configured to perform service data prediction on the next time period according to the target prediction model to obtain a service data prediction result.
Optionally, the model building module 100 may include:
the decomposition unit is used for decomposing the service data according to the STL algorithm to obtain a trend component, a seasonal component and a residual error component;
the function construction unit is used for performing function construction according to the trend component, the seasonal component and the residual error component to obtain a horizontal function, a trend function and a seasonal function;
a prediction function acquisition unit for determining a prediction function from the level function, the trend function, and the seasonal function;
and a prediction model acquisition unit for taking the level function, the trend function, the seasonal function, and the prediction function as a time series prediction model.
Optionally, the model modification module 200 may include:
the last time period prediction unit is used for performing prediction processing according to a prediction model constructed by the service data of the last time period to obtain prediction data of the current time period;
the deviation calculation unit is used for calculating the deviation according to actual data corresponding to the current time period in the service data and the predicted data of the current time period to obtain the deviation;
and the model correction unit is used for correcting the time sequence prediction model according to the deviation degree to obtain the target prediction model.
Optionally, the apparatus may include:
and the model updating module is used for updating the target prediction model according to the service data of the new time period when the service data of the new time period is received, so as to obtain a new target prediction model.
An embodiment of the present application further provides a server, including:
a memory for storing a computer program;
a processor for implementing the steps of the traffic data prediction method according to the above embodiments when executing the computer program.
The present application also provides a computer readable storage medium, where a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the computer program implements the steps of the business data prediction method according to the above embodiments.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The foregoing describes a service data prediction method, a service data prediction apparatus, a server, and a computer-readable storage medium provided by the present application in detail. The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
Claims (10)
1. A method for predicting service data, comprising:
the method comprises the steps of constructing a prediction model by taking service data as a time sequence according to a time sequence decomposition algorithm to obtain a time sequence prediction model;
correcting the time sequence prediction model according to the service data of the last time period to obtain a target prediction model;
and predicting the service data in the next time period according to the target prediction model to obtain a service data prediction result.
2. The business data prediction method of claim 1, wherein the step of constructing a prediction model by using the business data as a time sequence according to a time sequence decomposition algorithm to obtain a time sequence prediction model comprises the steps of:
decomposing the service data according to an STL algorithm to obtain a trend component, a seasonal component and a residual error component;
performing function construction according to the trend component, the seasonal component and the residual error component to obtain a horizontal function, a trend function and a seasonal function;
determining the prediction function from the level function, the trend function, and the seasonal function;
and taking the level function, the trend function, the seasonal function and the prediction function as the time sequence prediction model.
3. The service data prediction method according to claim 1, wherein the correcting the time sequence prediction model according to the service data of the previous time period to obtain a target prediction model comprises:
performing prediction processing according to a prediction model constructed according to the service data of the previous time period to obtain prediction data of the current time period;
calculating the deviation degree according to actual data corresponding to the current time period in the service data and the predicted data of the current time period to obtain the deviation degree;
and correcting the time sequence prediction model according to the deviation degree to obtain the target prediction model.
4. The traffic data prediction method according to any one of claims 1 to 3, further comprising:
and when the service data of a new time period is received, updating the target prediction model according to the service data of the new time period to obtain a new target prediction model.
5. A traffic data prediction apparatus, comprising:
the model construction module is used for constructing a prediction model by taking the service data as a time sequence according to a time sequence decomposition algorithm to obtain a time sequence prediction model;
the model correction module is used for correcting the time sequence prediction model according to the service data of the last time period to obtain a target prediction model;
and the data prediction module is used for predicting the service data in the next time period according to the target prediction model to obtain a service data prediction result.
6. The traffic data prediction device of claim 5, wherein the model building module comprises:
the decomposition unit is used for decomposing the service data according to an STL algorithm to obtain a trend component, a seasonal component and a residual error component;
the function construction unit is used for performing function construction according to the trend component, the seasonal component and the residual error component to obtain a horizontal function, a trend function and a seasonal function;
a prediction function acquisition unit configured to determine the prediction function from the level function, the trend function, and the seasonal function;
a prediction model acquisition unit configured to take the level function, the trend function, the seasonal function, and the prediction function as the time series prediction model.
7. The traffic data prediction device of claim 5, wherein the model modification module comprises:
the last time period prediction unit is used for performing prediction processing according to a prediction model constructed by the last time period service data to obtain prediction data of the current time period;
the deviation calculation unit is used for calculating the deviation according to actual data corresponding to the current time period in the service data and the predicted data of the current time period to obtain the deviation;
and the model correction unit is used for correcting the time sequence prediction model according to the deviation degree to obtain the target prediction model.
8. The traffic data prediction device according to any one of claims 5 to 7, further comprising:
and the model updating module is used for updating the target prediction model according to the service data of the new time period when the service data of the new time period is received, so as to obtain a new target prediction model.
9. A server, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the traffic data prediction method according to any of claims 1 to 4 when executing said computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the traffic data prediction method according to any one of claims 1 to 4.
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