CN108876421A - A kind of method and system for predicting commodity dynamic sales volume - Google Patents
A kind of method and system for predicting commodity dynamic sales volume Download PDFInfo
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Abstract
The present invention relates to a kind of method and system for predicting commodity dynamic sales volume, this method carries out dynamic prediction to Sales Volume of Commodity based on gray model and Catastrophe Model.This method does not need a large amount of historical data, solves the problems, such as medium-sized and small enterprises data deficiencies；And have very strong adaptability to fluctuation data and the time of exceptional value appearance can be effectively predicted, valuable data reference is provided for enterprise marketing decision.This method includes：Sales Volume of Commodity data are acquired, and the Sales Volume of Commodity data are pre-processed, wherein the pretreatment includes the catastrophe data detected in the Sales Volume of Commodity data, and rejects the catastrophe data；The corresponding vacancy numerical value of the catastrophe data being removed in Sales Volume of Commodity data described in polishing generates Sales Volume of Commodity data sequence；Unbiased grey-forecasting model is constructed using the Sales Volume of Commodity data sequence, and carries out Sales Volume of Commodity prediction using the unbiased grey-forecasting model.
Description
Technical field
The present invention relates to a kind of method and systems for predicting commodity dynamic sales volume.
Background technique
Important link of the Method for Sales Forecast as the marketing decision-making of e-commerce retailer and stock control is always people pass
The hot issue of note.High quality, high-precision sales forecast provide reliable data supporting for the decision of enterprise, to improve
The economic benefit of enterprise.And e-commerce retail market environment is complicated and changeable, the advertising campaign that rises one after another, consumer hobby
The factors such as transfer, the seasonal rhythm variation of commodity cause the diversity and variability of Sales Volume of Commodity feature, to make enterprise pair
The prediction of the market demand is more and more difficult.Many retailer maintain sales volume to stablize, have to constantly to guarantee customer demand
Increase the inventory of commodity, however the stock buildup of blindness can seriously affect the fund allotment of enterprise, waste corporate resources.How to close
Reason carries out the prediction of high quality to the commodity market demand changed at random, and then effectively controls commodity stocks, is current electric business
The major issue that retail business faces.
To the Method for Sales Forecast of single commodity, existing technical solution usually utilizes the history sales volume data of commodity, based on warp
The Classical forecast method of allusion quotation mathematics is predicted commonly mainly there are following three kinds of methods：
(1) it is weighted and averaged predicted method；
(2) (autoregression integrates moving average model(MA model) Auto Regressive Integrated Moving to ARIMA model
Average Model) predicted method；
(3) gray model (Gray Model, GM) predicted method.
In realizing process of the present invention, at least there are the following problems in the prior art for inventor's discovery：For weighted average
For predicted method, the computation model of the predicted method is relatively simple, and weight is not easy to determine, and the rule that do not fix can be followed,
It is random too strong, and it is difficult to cope with the sales volume data with Characteristics of Mutation, therefore be difficult to meet the requirements in accuracy；For
For ARIMA model prediction, which has preferable fitting effect to the data of law characteristic.But work as sales performance
When changing, order, auto-correlation coefficient and the order of PARCOR coefficients truncation of the difference of model are likely to become
Change, it means that model is poor to the biggish data generaliza-tion ability of fluctuation；For gray evaluation, predicted method warp
It is often used simple sequence first-order linear dynamic model GM (1,1), the model is suitable for catastrophe data (such as sales volume surge data)
Answering property is poor, and anti-interference ability is weak.And e-commerce retail trade market environment is complicated and changeable, necessarily causes merchandise sales special
The randomness of the frequent variation of sign, especially fashion and quick consumer lines industry, sales volume fluctuation is bigger, and prediction is more difficult.Therefore,
Necessarily prediction deviation is caused to increase using the prediction technique of single static, causes commodity supply shortage or commodity overstocking.
Summary of the invention
In view of this, the embodiment of the present invention provides a kind of method for predicting commodity dynamic sales volume, it is suitable for catastrophe data
Answering property is stronger, that is to say, that anti-interference ability is stronger, to realize more accurate Method for Sales Forecast.
According to an aspect of an embodiment of the present invention, a kind of method for predicting commodity dynamic sales volume, the method are provided
Including：Sales Volume of Commodity data are acquired, and the Sales Volume of Commodity data are pre-processed, wherein the pretreatment includes detection
Catastrophe data in the Sales Volume of Commodity data, and reject the catastrophe data；It is removed in Sales Volume of Commodity data described in polishing
The corresponding vacancy numerical value of catastrophe data, generate Sales Volume of Commodity data sequence；Nothing is constructed using the Sales Volume of Commodity data sequence
Inclined grey forecasting model, and Sales Volume of Commodity prediction is carried out using the unbiased grey-forecasting model.
Optionally, the catastrophe data include malice order, cancel an order and purchase by group the corresponding sales volume data of order.
Optionally, described pre-process further includes：The catastrophe number in the Sales Volume of Commodity data is detected according to Pauta criterion
According to the specific catastrophe numerical value of the determination catastrophe data and the time of occurrence of the catastrophe data.
Optionally, the corresponding vacancy numerical value of the catastrophe data being removed in Sales Volume of Commodity data described in polishing generates commodity
Sales volume data sequence further includes：It is corresponding using the catastrophe data being removed in Sales Volume of Commodity data described in average generation method polishing
Vacancy numerical value generates the Sales Volume of Commodity data sequence of prefixed time interval.
Optionally, the corresponding vacancy numerical value of the catastrophe data being removed in the Sales Volume of Commodity data described in polishing generates quotient
After product sales volume data sequence, the method also includes：Using acceleration translation transformation and logarithmic function transformation to the commodity pin
Amount data sequence optimizes.
Optionally, the method also includes：Time series is constructed using the time of occurrence of the catastrophe data, according to described
Time series constructs Catastrophe Model, to predict time that catastrophe occurs again.
Optionally, the method also includes：Using the unbiased grey-forecasting model and the Catastrophe Model combination into
It does business product Method for Sales Forecast.
Compared to traditional gray model GM (1,1), unbiased function GM (1,1) eliminates grey colo(u)r bias, avoids
The phenomenon that when the growth rate of initial data column is larger traditional gray model GM (1,1) being failed.In addition, without to nothing
The data sequence of inclined gray model GM (1,1) carries out regressive reduction.Therefore, unbiased function GM (1,1) improves stability.
According to another aspect of an embodiment of the present invention, a kind of system for predicting commodity dynamic sales volume, the system packet are provided
It includes：Acquisition module pre-processes, wherein the pre- place for acquiring Sales Volume of Commodity data, and to the Sales Volume of Commodity data
Reason includes the catastrophe data detected in the Sales Volume of Commodity data, and rejects the catastrophe data；Polishing module is used for polishing institute
The corresponding vacancy numerical value of the catastrophe data being removed in Sales Volume of Commodity data is stated, Sales Volume of Commodity data sequence is generated；Prediction module,
For using the Sales Volume of Commodity data sequence construct unbiased grey-forecasting model, and using the unbiased grey-forecasting model into
It does business product Method for Sales Forecast.
Optionally, the catastrophe data include malice order, cancel an order and purchase by group the corresponding sales volume data of order.
Optionally, the acquisition module is also used to：The catastrophe in the Sales Volume of Commodity data is detected according to Pauta criterion
Data, with the specific catastrophe numerical value of the determination catastrophe data and the time of occurrence of the catastrophe data.
Optionally, the polishing module is also used to：Using being removed in Sales Volume of Commodity data described in average generation method polishing
The corresponding vacancy numerical value of catastrophe data, generate the Sales Volume of Commodity data sequence of prefixed time interval.
Optionally, the system also includes：Optimization module, for quilt in the Sales Volume of Commodity data described in polishing module polishing
The corresponding vacancy numerical value of the catastrophe data of rejecting after generating Sales Volume of Commodity data sequence, utilizes acceleration translation transformation and logarithm
Sales Volume of Commodity data sequence described in function transform pairs optimizes.
Optionally, the system also includes：Hazard forecasting module, for being constructed using the time of occurrence of the catastrophe data
Time series constructs Catastrophe Model according to the time series, to predict time that catastrophe occurs again.
Optionally, the system is also used to：It is pre- using the unbiased grey-forecasting model and the catastrophe of the prediction module
The combination for surveying the Catastrophe Model of module carries out Sales Volume of Commodity prediction.
Using the method and system of the prediction commodity dynamic sales volume of the embodiment of the present invention, to traditional single Method for Sales Forecast
Technology is improved and has been broken through, and keeps the generalization ability of Method for Sales Forecast model stronger, can preferably cope with increasingly complex number
According to, and realize the prediction of high quality.
It is according to an embodiment of the present invention in another aspect, provide a kind of electronic equipment terminal, including：One or more processing
Device；For storing the storage device of one or more programs, when one or more of programs are by one or more of processing
Device executes, so that the method that one or more of processors realize provided prediction commodity dynamic sales volume according to the present invention.
It is according to an embodiment of the present invention in another aspect, provide a kind of computer-readable medium, be stored thereon with computer
Program realizes the method for prediction commodity dynamic sales volume provided by the present invention when described program is executed by processor.
Further effect possessed by above-mentioned non-usual optional way adds hereinafter in conjunction with specific embodiment
With explanation.
Detailed description of the invention
Fig. 1 is that the embodiment of the present invention can be applied to exemplary system architecture figure therein；
Fig. 2 is the main step of the method for the prediction commodity dynamic sales volume according to an embodiment of the present invention based on Multi-Model Combination
Rapid schematic diagram；
Fig. 3 is the flow diagram of the method for prediction commodity dynamic sales volume according to an embodiment of the present invention；
Fig. 4 is a kind of schematic diagram of system for predicting commodity dynamic sales volume according to an embodiment of the present invention；With
Fig. 5 is adapted for the structural schematic diagram for the computer system for realizing the electronic device terminal of the embodiment of the present application.
Specific embodiment
Present invention is further described in detail in the following with reference to the drawings and specific embodiments, but not as to limit of the invention
It is fixed.
Fig. 1 is shown can be using the prediction commodity dynamic sales volume method or prediction commodity dynamic sales volume of the embodiment of the present invention
The exemplary system architecture 100 of device.
As shown in Figure 1, system architecture 100 may include terminal device 101,102,103, network 104 and server 105.
Network 104 between terminal device 101,102,103 and server 105 to provide the medium of communication link.Network 104 can be with
Including various connection types, such as wired, wireless communication link or fiber optic cables etc..
User can be used terminal device 101,102,103 and be interacted by network 104 with server 105, to receive or send out
Send message etc..Various telecommunication customer end applications, such as the application of shopping class, net can be installed on terminal device 101,102,103
(merely illustrative) such as the application of page browsing device, searching class application, instant messaging tools, mailbox client, social platform softwares.
Terminal device 101,102,103 can be the various electronic equipments with display screen and supported web page browsing, packet
Include but be not limited to smart phone, tablet computer, pocket computer on knee and desktop computer etc..
Server 105 can be to provide the server of various services, such as utilize terminal device 101,102,103 to user
The shopping class website browsed provides the back-stage management server (merely illustrative) supported.Back-stage management server can be to reception
To the data such as information query request analyze etc. processing, and by processing result (such as target push information, product letter
Breath -- merely illustrative) feed back to terminal device.
It should be noted that prediction commodity dynamic sales volume method is generally by server 105 provided by the embodiment of the present invention
It executes, correspondingly, prediction commodity dynamic sales volume device is generally positioned in server 105.
It should be understood that the number of terminal device, network and server in Fig. 1 is only schematical.According to realization need
It wants, can have any number of terminal device, network and server.
Fig. 2 is the schematic diagram of the key step of the method for the prediction commodity dynamic sales volume based on Multi-Model Combination.Such as Fig. 2 institute
Show, Fig. 2 shows a kind of methods for predicting commodity dynamic sales volume comprising following steps：
Step S201：Sales Volume of Commodity data are acquired, and the Sales Volume of Commodity data are pre-processed, wherein is described pre-
Processing includes the catastrophe data detected in the Sales Volume of Commodity data, and rejects the catastrophe data.In the embodiment of the present invention, calamity
Parameter is according to can be, but not limited to includes malice order, cancel an order and purchases by group the corresponding sales volume data of order.Above-mentioned pretreatment tool
Body may also include：According to Pauta criterion, (La Yida, that is, PauTa, Pauta criterion are first to assume that one group of detection data contains only
Random error carries out calculation processing to it and obtains standard deviation, by one section of certain determine the probability, it is believed that all is more than this area
Between error, be just not belonging to random error but gross error, the data containing the error should give rejecting.This differentiation processing
Principle and method are limited only to the processing of the sample data of normal state or approximate normal distribution, it be with pendulous frequency sufficiently greatly before
It mentions, it is less reliable for rejecting gross error with criterion when the situation of pendulous frequency.Therefore, the situation less in pendulous frequency
Under, the criterion had better not be selected) catastrophe data in the detection Sales Volume of Commodity data, with the tool of the determination catastrophe data
The time of occurrence of body catastrophe numerical value and the catastrophe data.
Step S202：The corresponding vacancy numerical value of the catastrophe data being removed in Sales Volume of Commodity data described in polishing generates quotient
Product sales volume data sequence.Rejecting belongs to after the exceptional value of catastrophe data, will appear in the sequence of the article sales data of acquisition
Vacancy, and when subsequent builds Method for Sales Forecast model, need to use the ordered series of numbers arranged according to prefixed time interval, therefore, the present invention
In embodiment, the corresponding vacancy number of the catastrophe data being removed in Sales Volume of Commodity data described in average generation method polishing can use
Value, generates the Sales Volume of Commodity data sequence of prefixed time interval.
In addition, in the embodiment of the present invention, the corresponding sky of catastrophe data that is removed in the Sales Volume of Commodity data described in polishing
Bit value, after generating Sales Volume of Commodity data sequence, the method may also include：Become using acceleration translation transformation and logarithmic function
It changes and the Sales Volume of Commodity data sequence is optimized.
Step S203：Unbiased grey-forecasting model is constructed using the Sales Volume of Commodity data sequence, and is utilized described unbiased
Grey forecasting model carries out Sales Volume of Commodity prediction.
In addition, the method for the prediction commodity dynamic sales volume of the embodiment of the present invention may also include：Utilize the catastrophe data
Time of occurrence constructs time series, constructs Catastrophe Model according to the time series, to predict time that catastrophe occurs again.And
And after building Catastrophe Model, commodity are carried out using the combination of the unbiased grey-forecasting model and the Catastrophe Model
Method for Sales Forecast.
Fig. 3 is the flow diagram of the method for prediction commodity dynamic sales volume according to an embodiment of the present invention.Below according to Fig. 3
The detailed process of the method for the prediction commodity dynamic sales volume of the embodiment of the present invention is described in detail.
As shown in figure 3, the detailed process of the method for the prediction commodity dynamic sales volume of the embodiment of the present invention mainly includes as follows
Several parts：
Step S1：Data preparation, the data for carrying out Sales Volume of Commodity data acquire and data preparation, do not have by rejecting
Reflect the sales volume data x of market real demand to pre-process to data sample.Reflect the true of market by this method
Demand, the as far as possible influence of exclusive PCR factor.
Step S2：Rejecting outliers, because of sales volume data approximation Normal Distribution, that is, P (| x- μ | 3 σ of >)≤
0.003, μ and σ in formula respectively indicate the mathematic expectaion and variance of normal distribution totality, and x is original sales volume data, and P is can
Energy property, it is possible to exceptional value is determined using La Yida (PauTa) criterion, at this point, occurring in original sales volume data big
In+3 σ of μ or data less than μ -3 σ be small probability event.Therefore ,+3 σ of μ is greater than we can determine whether all according to the criterion
Or the data less than μ -3 σ are exceptional value, while determining the time series t (i) that exceptional value occurs, and reject these exceptions
Value.The time series t (i) for constructing Catastrophe Model in step s 5.
Step S3：It is data-optimized, it is constant duration since gray model GM (1,1) requires ordered series of numbers, and due to exception
The rejecting of value is so that vacancy occur in original sales volume data, so needing further to utilize " average generation method " right after step S2
The vacancy numerical value being removed carries out polishing, to generate the sequence of constant duration.The principle of average generation method is：
It is assumed that original sales volume data sequence is X0=(x1,x2,…,xk-1,xk,xk+1,…,xn), and xkIt detects
Exceptional value, then just by exceptional value xkIt rejects, utilizes new numerical valuePolishing is carried out to vacancy numerical value,
It is to obtain new sales volume data sequence：
In data-optimized step can also using accelerate translation transformation and logarithmic function transformation come the equal times to generation
The sequence at interval optimizes, and mode is respectively：
Accelerating translation transformation is by initial sales volume data sequence X=(x1,x2,…,xn) pass through following formula：
x'k=x (k)+(k-1)T, (k=1,2,3 ..., n),
Wherein, T=M-m
M=max x (k), k=1,2,3 ..., n }
M=min x (k), k=1,2,3 ..., n }
It is transformed to sequence X '=(x'1,x'2,…,x'n)。
Logarithmic function transformation is by initial sales volume data sequence X=(x1,x2,…,xn) pass through following formula：
x'k=ln (x (k)), (k=1,2,3 ..., n),
It is transformed to sequence X '=(x'1,x'2,…,x'n)。
Step S4：Method for Sales Forecast model is constructed, utilizes the sequence construct grey of the constant duration generated in this step
Model GM (1,1), to predict sales volume data.In this step, can use the sequence construct of the constant duration of generation without
Inclined gray model GM (1,1), to predict sales volume data.
Wherein, the step of building (1,1) unbiased function GM is：
It is assumed that original sales volume data sequence is X=(x1,x2,…,xn), and assume it is optimized after sales volume data sequence
ForWherein,I=1,2,3 ..., n.
Step1:It calculates single order Accumulating generation sequence (1-AGO)
To obtain
Step2:Determine data matrix A and
Step3:Pass through formulaParameter value a and μ are obtained,
Step4:Seek the parameter of unbiased GM (1,1) prediction model
Step5:Establish the prediction model of original sales volume data sequence：
It is the predicted value of original sales volume data sequence.
Compared to traditional gray model GM (1,1), unbiased function GM (1,1) eliminates grey colo(u)r bias, avoids
The phenomenon that when the growth rate of initial data column is larger traditional gray model GM (1,1) being failed.In addition, without to nothing
The data sequence of inclined gray model GM (1,1) carries out regressive reduction.Therefore, unbiased function GM (1,1) improves stability.
Step S5：Catastrophe Model is constructed, the time sequence occurred in this step using exceptional value determining in step s 2
Column t (i) construct Catastrophe Model, to predict the time that catastrophe occurs again.
The catastrophe mould constructed by (unbiased) the gray model GM (1,1) that constructs in step s 4 and in step s 5
Type predicts time that Sales Volume of Commodity and catastrophe occur again, to achieve the purpose that integrated forecasting.
Fig. 4 shows a kind of system 400 for predicting commodity dynamic sales volume comprising：Acquisition module 401, polishing module 402
And prediction module 403.
Wherein, acquisition module 401 is for acquiring Sales Volume of Commodity data, and pre-processes to the Sales Volume of Commodity data,
Wherein, the pretreatment includes the catastrophe data detected in the Sales Volume of Commodity data, and rejects the catastrophe data；Polishing mould
Catastrophe data corresponding vacancy numerical value of the block 402 for being removed in Sales Volume of Commodity data described in polishing, generates Sales Volume of Commodity number
According to sequence；Prediction module 403 is used to construct unbiased grey-forecasting model using the Sales Volume of Commodity data sequence, and described in utilization
Unbiased grey-forecasting model carries out Sales Volume of Commodity prediction.
Wherein, above-mentioned catastrophe data include malice order, cancel an order and purchase by group the corresponding sales volume data of order.
In addition, acquisition module 401 can also be used in：The catastrophe number in the Sales Volume of Commodity data is detected according to Pauta criterion
According to the specific catastrophe numerical value of the determination catastrophe data and the time of occurrence of the catastrophe data.
Polishing module 402 can also be used in：Utilize the catastrophe being removed in Sales Volume of Commodity data described in average generation method polishing
The corresponding vacancy numerical value of data, generates the Sales Volume of Commodity data sequence of prefixed time interval.
In addition, system 400 may also include：Optimization module (not shown) is used for the commodity described in polishing module polishing
The corresponding vacancy numerical value of the catastrophe data being removed in sales volume data is flat using accelerating after generating Sales Volume of Commodity data sequence
It moves transformation and logarithmic function transformation optimizes the Sales Volume of Commodity data sequence.
In addition, system 400 may also include：Hazard forecasting module (not shown), for utilizing the catastrophe data
Time of occurrence constructs time series, constructs Catastrophe Model according to the time series, to predict time that catastrophe occurs again.And
And system 400 can also be used in, unbiased grey-forecasting model and the hazard forecasting module using the prediction module 403
The combination of Catastrophe Model carries out Sales Volume of Commodity prediction.
Below with reference to Fig. 5, it illustrates the departments of computer science for the electronic device terminal for being suitable for being used to realize the embodiment of the present application
The structural schematic diagram of system 500.Electronic device terminal shown in Fig. 5 is only an example, should not be to the function of the embodiment of the present application
Any restrictions can be brought with use scope.
As shown in figure 5, computer system 500 includes central processing unit (CPU) 501, it can be read-only according to being stored in
Program in memory (ROM) 502 or be loaded into the program in random access storage device (RAM) 503 from storage section 508 and
Execute various movements appropriate and processing.In RAM 503, also it is stored with system 500 and operates required various programs and data.
CPU 501, ROM 502 and RAM 503 are connected with each other by bus 504.Input/output (I/O) interface 505 is also connected to always
Line 504.
I/O interface 505 is connected to lower component：Importation 506 including keyboard, mouse etc.；It is penetrated including such as cathode
The output par, c 507 of spool (CRT), liquid crystal display (LCD) etc. and loudspeaker etc.；Storage section 508 including hard disk etc.；
And the communications portion 509 of the network interface card including LAN card, modem etc..Communications portion 509 via such as because
The network of spy's net executes communication process.Driver 510 is also connected to I/O interface 505 as needed.Detachable media 511, such as
Disk, CD, magneto-optic disk, semiconductor memory etc. are mounted on as needed on driver 510, in order to read from thereon
Computer program be mounted into storage section 508 as needed.
Particularly, disclosed embodiment, the process described above with reference to flow chart may be implemented as counting according to the present invention
Calculation machine software program.For example, embodiment disclosed by the invention includes a kind of computer program product comprising be carried on computer
Computer program on readable medium, the computer program include the program code for method shown in execution flow chart.?
In such embodiment, which can be downloaded and installed from network by communications portion 509, and/or from can
Medium 511 is dismantled to be mounted.When the computer program is executed by central processing unit (CPU) 501, the system that executes the application
The above-mentioned function of middle restriction.
It should be noted that computer-readable medium shown in the application can be computer-readable signal media or meter
Calculation machine readable storage medium storing program for executing either the two any combination.Computer readable storage medium for example can be --- but not
Be limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor system, device or device, or any above combination.Meter
The more specific example of calculation machine readable storage medium storing program for executing can include but is not limited to：Electrical connection with one or more conducting wires, just
Taking formula computer disk, hard disk, random access storage device (RAM), read-only memory (ROM), erasable type may be programmed read-only storage
Device (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic memory device,
Or above-mentioned any appropriate combination.In this application, computer readable storage medium can be it is any include or storage journey
The tangible medium of sequence, the program can be commanded execution system, device or device use or in connection.And at this
In application, computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium other than storage medium is read, which can send, propagates or transmit and be used for
By the use of instruction execution system, device or device or program in connection.Include on computer-readable medium
Program code can transmit with any suitable medium, including but not limited to：Wirelessly, electric wire, optical cable, RF etc. or above-mentioned
Any appropriate combination.
Flow chart and block diagram in attached drawing are illustrated according to the system of the various embodiments of the application, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of above-mentioned module, program segment or code include one or more
Executable instruction for implementing the specified logical function.It should also be noted that in some implementations as replacements, institute in box
The function of mark can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are practical
On can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it wants
It is noted that the combination of each box in block diagram or flow chart and the box in block diagram or flow chart, can use and execute rule
The dedicated hardware based systems of fixed functions or operations is realized, or can use the group of specialized hardware and computer instruction
It closes to realize.
Being described in module involved in the embodiment of the present application can be realized by way of software, can also be by hard
The mode of part is realized.Described module also can be set in the processor, for example, can be described as：A kind of processor packet
Include acquisition module, polishing module and prediction module.Wherein, the title of these modules is not constituted under certain conditions to the mould
The restriction of block itself, for example, acquisition module is also described as " for acquiring the module of Sales Volume of Commodity data ".
As on the other hand, present invention also provides a kind of computer-readable medium, which be can be
Included in electronic device terminal described in above-described embodiment；It is also possible to individualism, and is set without the electronics is incorporated
In standby terminal.Above-mentioned computer-readable medium carries one or more program, when said one or multiple programs are by one
When a electronic device terminal executes, so that the electronic device terminal includes：Sales Volume of Commodity data are acquired, and to the commodity pin
Amount data are pre-processed, wherein the pretreatment includes the catastrophe data detected in the Sales Volume of Commodity data, and rejects institute
State catastrophe data；The corresponding vacancy numerical value of the catastrophe data being removed in Sales Volume of Commodity data described in polishing generates Sales Volume of Commodity
Data sequence；Unbiased grey-forecasting model is constructed using the Sales Volume of Commodity data sequence, and utilizes the unbiased gray prediction
Model carries out Sales Volume of Commodity prediction.
Technical solution according to an embodiment of the present invention is improved and has been dashed forward to traditional single Method for Sales Forecast technology
It is broken, keep the generalization ability of Method for Sales Forecast model stronger, can preferably cope with increasingly complex data, and realize high quality
Prediction.
Method provided by the embodiment of the present invention can be performed in the said goods, has the corresponding functional module of execution method and has
Beneficial effect.The not technical detail of detailed description in the present embodiment, reference can be made to method provided by the embodiment of the present invention.
Although the embodiment of the present invention is described in detail in conjunction with attached drawing, embodiment is only used for the explanation and illustration present invention,
Rather than for limiting the present invention.The scope of the present invention is defined by the claims, change, replacement of some of them element etc.
It is obvious.
Claims (16)
1. a kind of method for predicting commodity dynamic sales volume, which is characterized in that the method includes：
Sales Volume of Commodity data are acquired, and the Sales Volume of Commodity data are pre-processed, wherein the pretreatment includes detection institute
The catastrophe data in Sales Volume of Commodity data are stated, and reject the catastrophe data；
The corresponding vacancy numerical value of the catastrophe data being removed in Sales Volume of Commodity data described in polishing generates Sales Volume of Commodity data sequence
Column；
Using the Sales Volume of Commodity data sequence construct unbiased grey-forecasting model, and using the unbiased grey-forecasting model into
It does business product Method for Sales Forecast.
2. the method according to claim 1, wherein the catastrophe data include malice order, cancel an order and
Purchase by group the corresponding sales volume data of order.
3. the method according to claim 1, wherein the pretreatment further includes：It is detected according to Pauta criterion
Catastrophe data in the Sales Volume of Commodity data, with the specific catastrophe numerical value of the determination catastrophe data and the catastrophe data
Time of occurrence.
4. the method according to claim 1, wherein the catastrophe number being removed in Sales Volume of Commodity data described in polishing
According to corresponding vacancy numerical value, generating Sales Volume of Commodity data sequence further includes：
Using the corresponding vacancy numerical value of catastrophe data being removed in Sales Volume of Commodity data described in average generation method polishing, generate pre-
If the Sales Volume of Commodity data sequence of time interval.
5. the method according to claim 1, wherein the catastrophe being removed in the Sales Volume of Commodity data described in polishing
The corresponding vacancy numerical value of data, after generating Sales Volume of Commodity data sequence, the method also includes：Using accelerate translation transformation and
Logarithmic function transformation optimizes the Sales Volume of Commodity data sequence.
6. the method according to claim 1, wherein the method also includes：Utilize going out for the catastrophe data
Time series is constructed between current, Catastrophe Model is constructed according to the time series, to predict time that catastrophe occurs again.
7. according to the method described in claim 6, it is characterized in that, the method also includes：Utilize the unbiased gray prediction
The combination of model and the Catastrophe Model carries out Sales Volume of Commodity prediction.
8. a kind of system for predicting commodity dynamic sales volume, which is characterized in that the system comprises：
Acquisition module pre-processes, wherein described pre- for acquiring Sales Volume of Commodity data, and to the Sales Volume of Commodity data
Processing includes the catastrophe data detected in the Sales Volume of Commodity data, and rejects the catastrophe data；
Polishing module, the corresponding vacancy numerical value of catastrophe data for being removed in Sales Volume of Commodity data described in polishing generate quotient
Product sales volume data sequence；
Prediction module for constructing unbiased grey-forecasting model using the Sales Volume of Commodity data sequence, and utilizes described unbiased
Grey forecasting model carries out Sales Volume of Commodity prediction.
9. system according to claim 8, which is characterized in that the catastrophe data include malice order, cancel an order and
Purchase by group the corresponding sales volume data of order.
10. system according to claim 8, which is characterized in that the acquisition module is also used to：It is examined according to Pauta criterion
The catastrophe data in the Sales Volume of Commodity data are surveyed, with the specific catastrophe numerical value and the catastrophe number of the determination catastrophe data
According to time of occurrence.
11. system according to claim 8, which is characterized in that the polishing module is also used to：
Using the corresponding vacancy numerical value of catastrophe data being removed in Sales Volume of Commodity data described in average generation method polishing, generate pre-
If the Sales Volume of Commodity data sequence of time interval.
12. system according to claim 8, which is characterized in that the system also includes：Optimization module, in polishing
The corresponding vacancy numerical value of the catastrophe data being removed in Sales Volume of Commodity data described in module polishing generates Sales Volume of Commodity data sequence
Later, the Sales Volume of Commodity data sequence is optimized using acceleration translation transformation and logarithmic function transformation.
13. system according to claim 8, which is characterized in that the system also includes：Hazard forecasting module, for benefit
Time series is constructed with the time of occurrence of the catastrophe data, Catastrophe Model is constructed according to the time series, to predict catastrophe
The time occurred again.
14. system according to claim 13, which is characterized in that the system is also used to：Utilize the prediction module
The combination of unbiased grey-forecasting model and the Catastrophe Model of the hazard forecasting module carries out Sales Volume of Commodity prediction.
15. a kind of electronic equipment terminal, which is characterized in that including：
One or more processors；
Storage device, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
The now method as described in any in claim 1-7.
16. a kind of computer-readable medium, is stored thereon with computer program, which is characterized in that described program is held by processor
The method as described in any in claim 1-7 is realized when row.
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