CN115640917A - Method, apparatus, device, medium, and program product for generating demand for goods - Google Patents
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Abstract
Embodiments of the present disclosure disclose an item demand generation method, apparatus, device, medium, and program product. One specific implementation mode of the method comprises the steps of determining whether an article to be predicted is an intermittent demand article according to a demand interval parameter of the article to be predicted; in response to the fact that the article to be predicted is determined to be an intermittent demand article, a pre-trained model pool and an elastic network are obtained, wherein a model in the model pool is used for outputting a prediction demand, and the elastic network is used for outputting the article demand of the article to be predicted according to the prediction demand of each model in the model pool; and generating the article demand of the article to be predicted by utilizing the model pool and the elastic network. The embodiment realizes the improvement of the accuracy of the generated demand quantity prediction of the goods.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to a method, an apparatus, a device, a medium, and a program product for generating a demand for goods.
Background
Demand forecasting is an important basis for planning activities such as inventory management. In many cases, the demand may be sparse and intermittent over a period of time, i.e., there may be some zero demand, especially in some aftermarket industries. For such intermittent demand prediction, a single model is usually used for prediction in the related prediction methods.
Then, the inventors found that when the intermittent demand prediction is performed in the above manner, there are often the following technical problems:
the problem of low pre-storage accuracy exists when a single model is used for prediction.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Some embodiments of the present disclosure propose item demand generation methods, apparatuses, devices, media, and program products to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide an item demand generation method, including: determining whether the article to be predicted is an intermittent article to be predicted or not according to the demand interval parameter of the article to be predicted; in response to the fact that the article to be predicted is determined to be an intermittent demand article, a pre-trained model pool and an elastic network are obtained, wherein a model in the model pool is used for outputting a prediction demand, and the elastic network is used for outputting the article demand of the article to be predicted according to the prediction demand of each model in the model pool; and generating the article demand of the article to be predicted by utilizing the model pool and the elastic network.
In a second aspect, some embodiments of the present disclosure provide an item demand generation apparatus, including: the determining unit is configured to determine whether the article to be predicted is an intermittent demand article according to the demand interval parameter of the article to be predicted; the system comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is configured to respond to the fact that an article to be predicted is an intermittent demand article, acquire a pre-trained model pool and an elastic network, a model in the model pool is used for outputting a prediction demand, and the elastic network is used for outputting the article demand of the article to be predicted according to the prediction demand of each model in the model pool; and the generation unit is configured to generate the article demand of the article to be predicted by using the model pool and the elastic network.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium on which a computer program is stored, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
In a fifth aspect, some embodiments of the present disclosure provide a computer program product comprising a computer program that, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The above embodiments of the present disclosure have the following advantages: the accuracy of the generated item demand is improved by fusing the predicted demand of each model in the model pool. Specifically, the reason why the accuracy of the related prediction method is not high is that: a single model is used for prediction. Based on this, the method for generating the demand quantity of the goods according to some embodiments of the present disclosure predicts by using a plurality of models in the model pool, and performs fusion of prediction sets through the elastic network on this basis, so that the prediction results of each model can be integrated, the advantages of each model can be exerted, and the prediction accuracy can be improved.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a schematic diagram of one application scenario of an item demand generation method, according to some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of an item demand generation method according to the present disclosure;
FIG. 3 is a flow chart diagram of a method of training a model pool and an elastic network in a commodity demand generation method according to the present disclosure;
FIG. 4 is a schematic block diagram of some embodiments of an item demand generation apparatus according to the present disclosure;
FIG. 5 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of an application scenario of an item demand generation method according to some embodiments of the present disclosure.
As shown in fig. 1, an executing body of the method for generating the demand quantity of the goods may first determine whether the goods to be predicted is intermittent demand goods according to a demand interval parameter 101 of the goods to be predicted. In response to determining that the item to be predicted is an intermittent demand item, a pool of pre-trained models 102 and elastic network 103 are obtained. By way of example, the models in the model pool 102 include Croston, SBA, SBJ, lightGBM. The models are used for outputting predicted demand, and the elastic network 103 is used for outputting the item demand of the item to be predicted according to the predicted demand of each model in the model pool. On the basis, the model pool 102 and the elastic network 103 are used for generating the item demand 104 of the item to be predicted.
With continued reference to fig. 2, a flow 200 of some embodiments of an item demand generation method according to the present disclosure is shown. The method for generating the demand quantity of the goods comprises the following steps:
In practice, the demand for some items is continuous at different points in time over a period of time. For example, for "paper towels", there is a certain sales volume per day in a month, i.e. there is a demand volume per day, and then "paper towels" can be considered as a continuity demand item. Corresponding to the above, the article is an intermittent article. In order to accurately distinguish a continuous demand item from a discontinuous demand item, a demand interval parameter is introduced.
In practice, as an example, the demand interval parameter may be an average demand interval. As another example, the demand interval parameters may include an average demand interval and a zero demand duty ratio.
The average demand interval may be an average of interval durations between two adjacent non-zero demands in a period of time. For example, the daily demand is 1, 0, 2, 0, 5, 0, 4 from 1 month 1 to 1 month 10. The interval duration between 1 month and 4 months is 2,1 and 6 months, and the interval duration between 1 month and 4 days is 1,1 and 10 months, and is 3, and the average interval duration is 2. The zero demand occupation ratio is the occupation ratio of the number of the zero demand quantities in a period of time to the total demand quantity. In this case, the zero demand ratio is 60%.
In some embodiments, an executive body (which may be various electronic devices) of the item demand generation method may determine whether the item to be predicted is an intermittent demand item according to a demand interval parameter of the item to be predicted. Optionally, if the value of the average demand interval is greater than or equal to the preset demand interval threshold, the article to be predicted may be considered to be an intermittent demand article. If the average demand interval value is smaller than the preset demand interval threshold value, the article to be predicted is not an intermittent demand article but a continuous demand article. In addition, according to actual needs, the value of the maximum demand interval can be compared with a preset demand interval threshold value, and whether the article to be predicted is an intermittent demand article or not is determined. For example, if the value of the maximum demand interval is greater than or equal to the preset demand interval threshold, the article to be predicted may be considered to be an intermittent demand article.
In some optional implementation manners of some embodiments, on the basis of determining that the article to be predicted is an intermittent article in need, the need sparsity degree value of the article to be predicted may be further determined. Specifically, in response to determining that the article to be predicted is an intermittent demand article, acquiring a pre-trained model pool and an elastic network, including: in response to the fact that the article to be predicted is determined to be the intermittent demand article, determining a demand sparsity degree value of the article to be predicted; and responding to the fact that the requirement sparsity degree value is larger than a preset sparsity degree threshold value, and obtaining a model pool and an elastic network which are trained in advance. The larger the demand sparsity degree value is, the more sparsely the demand amount of the article is. Due to the fact that the demand sparse degree values are different, the applicable demand forecasting method is different, and therefore the accuracy of demand forecasting is different. Based on this, when it is determined that the required sparsity degree value is greater than the preset sparsity degree threshold, the subsequent steps are performed, that is, the pre-trained model pool and the elastic network are obtained. In the case where it is determined that the required sparsity degree value is less than or equal to the preset sparsity degree threshold, the subsequent steps may not be performed. Wherein, the demand sparsity degree value can be represented by a zero demand proportion.
In some embodiments, the model pool and elastic network may be pre-trained. The models in the model pool may be models suitable for demand quantity prediction of discontinuously demanded goods, and the models in the model pool may be statistical time series models or Machine learning models, such as Croston algorithm and its variants, SBA, SBJ, lightGBM (Light Gradient Boosting Machine-based distributed Gradient Boosting framework) and the like. Croston proposes a combined prediction scheme, which is characterized in that the interval between the non-zero demand and the demand is predicted separately, and the final time series prediction result is formed through the predicted interval and the predicted demand. SBA (synchronous-Boylan optimization), TSB (discontinuous demand algorithm) are all modified by some coefficients on the basis of Croston, so that the estimation is unbiased. Each model in the pool of models may output a predicted demand.
In some embodiments, the elastic network is configured to output the item demand of the item to be forecasted according to the forecasted demand of each model in the model pool. For example, the predicted demand of each model in the model pool may be weighted to obtain the item demand of the item to be predicted. Wherein the weight of each model can be determined in the model training process.
In some embodiments, the elastic network (Elasticenet) is a linear regression model, which is a combination of ridge regression and Lasso regression.
And step 203, generating the article demand of the article to be predicted by using the model pool and the elastic network.
In some embodiments, characteristics of the item to be predicted, which are related to demand prediction, may be input into each model in the model pool, resulting in a plurality of actual predicted values. And then, inputting the actual predicted values into an elastic network to obtain the article demand of the article to be predicted.
In some optional implementations of some embodiments, the method further comprises: and controlling the replenishment equipment to replenish the to-be-predicted articles according to the article demand. Therefore, inventory control cost can be saved, and meanwhile, the shortage risk can be avoided through accurate judgment of the demand.
In some embodiments, the accuracy of the generated demand for the item is improved by fusing the predicted demands of the individual models in the model pool. Specifically, the reason why the accuracy of the related prediction method is not high is that: a single model is used for prediction. Based on this, the method for generating the demand quantity of the goods according to some embodiments of the present disclosure performs prediction by using a plurality of models in the model pool, and performs fusion of prediction sets through the elastic network on the basis, so that the prediction results of each model can be integrated, the advantages of each model can be exerted, and the prediction accuracy can be improved.
With continued reference to fig. 3, a flow 300 of a method of training a model pool and an elastic network in a method of item demand generation is shown, the method of training comprising the steps of:
In some embodiments, the execution subject of the training method may or may not be consistent with the execution subject of the item demand generation method. An executing subject of the training method may obtain a sample set. The sample set comprises a training sample set and a verification sample set. Wherein the samples in the sample set include a demand impact characteristic and a true demand. In practice, the demand impact characteristics may include item attributes, date information, weather information, promotional discount information, and the like.
Optionally, the sample set may further include a test sample set for testing the prediction capability of the model.
In some implementations, the performing agent may train each initial model into the pool of initial models based on samples in the training sample set. As an example, each initial model may be trained in a back propagation and random gradient descent manner until a training end condition is satisfied, thereby obtaining a model pool.
And 303, respectively inputting the demand influence characteristics in the samples in the verification sample set into each model in the model pool to obtain a plurality of demand predicted values.
In some embodiments, the executing entity may input the feature of the demand impact in the sample in the verification sample set into each model in the model pool obtained in step 302, respectively, to obtain a plurality of demand predicted values output by each model.
And step 304, taking the multiple predicted values of the demand quantity as the input of the initial elastic network, taking the real demand quantity in the samples in the verification sample set as the expected output of the initial elastic network, and training the initial elastic network to obtain the elastic network.
In some embodiments, the execution subject may use a plurality of predicted demand values as an input of the initial elastic network, use a real demand corresponding to the input sample as an expected output, and adjust a network parameter of the initial elastic network, so as to train the initial elastic network until a training stop condition is satisfied, thereby obtaining the elastic network. Wherein, due to training, the elastic network learns the proper weight, thereby weighting the predicted demand of each model with the proper weight. In some embodiments, the loss function may be optimized by a gradient descent algorithm. The loss function may take the Sum of Squared Errors (SSE) to determine the parameter at which SSE is minimal. Because the elastic network is a combination of ridge regression and Lasso regression, the SSE includes an L1 regularization term and an L2 regularization term, so that the elastic network not only retains the feature selection characteristic of the Lasso regression, but also considers the stability of the ridge regression.
In some embodiments, the elastic network learns the appropriate weights due to training, so that the predicted demand of each model is weighted by the appropriate weights to improve the accuracy of predicting the demand of the goods.
With further reference to fig. 4, as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an article demand prediction model generation apparatus, which correspond to those of the method shown in fig. 2, and which can be applied in various electronic devices.
As shown in fig. 4, the item demand generation apparatus 400 of some embodiments includes: the determining unit 401 is configured to determine whether the item to be predicted is an intermittent demand item according to the demand interval parameter of the item to be predicted. The obtaining unit 402 is configured to obtain a pre-trained model pool and an elastic network in response to determining that the article to be predicted is an intermittent demand article, wherein the models in the model pool are used for outputting predicted demand, and the elastic network is used for outputting article demand of the article to be predicted according to the predicted demand of each model in the model pool. The generation unit 403 is configured to generate the item demand of the item to be predicted, using the model pool and the elastic network.
In some optional implementations of some embodiments, the obtaining unit 402 is further configured to: in response to the fact that the article to be predicted is determined to be the intermittent demand article, determining a demand sparsity degree value of the article to be predicted; and responding to the fact that the requirement sparsity degree value is larger than a preset sparsity degree threshold value, and obtaining a model pool and an elastic network which are trained in advance.
In some optional implementations of some embodiments, the model pool and the elastic network are trained by: acquiring a sample set, wherein the sample set comprises a training sample set and a verification sample set, and samples in the sample set comprise demand influence characteristics and real demand; training the initial model in the initial model pool based on the training sample set to obtain a model pool; respectively inputting the demand influence characteristics in the samples in the verification sample set into each model in the model pool to obtain a plurality of demand predicted values; and taking the multiple predicted values of the demand quantity as the input of the initial elastic network, taking the real demand quantity in the samples in the verification sample set as the expected output of the initial elastic network, and training the initial elastic network to obtain the elastic network.
In some optional implementations of some embodiments, the demand interval parameter includes an average demand interval; and the determining unit 401 is further configured to: and in response to determining that the value of the average demand interval of the to-be-predicted items is greater than or equal to a preset demand interval threshold, determining that the to-be-predicted items are intermittent demand items.
In some optional implementations of some embodiments, the item demand generation apparatus 400 further includes: a replenishment unit configured to: and controlling the replenishment equipment to replenish the to-be-predicted articles according to the article demand.
It is understood that the units described in the apparatus 400 correspond to the respective steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are equally applicable to the apparatus 400 and the units included therein and will not be described in detail here.
Referring now to FIG. 5, a block diagram of an electronic device (e.g., the computing device of FIG. 1) 500 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506, including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc., include output devices 505, such as Liquid Crystal Displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: determining whether the article to be predicted is an intermittent article to be predicted or not according to the demand interval parameter of the article to be predicted; in response to the fact that the article to be predicted is determined to be an intermittent demand article, a pre-trained model pool and an elastic network are obtained, wherein a model in the model pool is used for outputting a prediction demand, and the elastic network is used for outputting the article demand of the article to be predicted according to the prediction demand of each model in the model pool; and generating the article demand of the article to be predicted by utilizing the model pool and the elastic network.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes a sample set determination unit, an acquisition unit, and a generation unit. Where the names of these units do not in some cases constitute a limitation of the unit itself, for example, the determination unit may also be described as a "unit that determines whether the item to be predicted is an intermittently demanded item".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. An item demand generation method, comprising:
determining whether the article to be predicted is an intermittent article to be predicted or not according to the demand interval parameter of the article to be predicted;
in response to the fact that the article to be predicted is determined to be an intermittent demand article, obtaining a pre-trained model pool and an elastic network, wherein models in the model pool are used for outputting predicted demand, and the elastic network is used for outputting the article demand of the article to be predicted according to the predicted demand of each model in the model pool;
and generating the article demand quantity of the article to be predicted by utilizing the model pool and the elastic network.
2. The method of claim 1, wherein said obtaining a pool of pre-trained models and an elastic network in response to determining that the item to be predicted is an intermittent demand item comprises:
in response to determining that the article to be predicted is an intermittent demand article, determining a demand sparsity degree value of the article to be predicted;
and responding to the fact that the requirement sparsity degree value is larger than a preset sparsity degree threshold value, and obtaining a model pool and an elastic network which are trained in advance.
3. The method of claim 1, wherein the model pool and the elastic network are trained by:
acquiring a sample set, wherein the sample set comprises a training sample set and a verification sample set, and samples in the sample set comprise demand influence characteristics and real demand;
training initial models in an initial model pool based on the training sample set to obtain the model pool;
respectively inputting the demand influence characteristics in the samples in the verification sample set into each model in the model pool to obtain a plurality of demand predicted values;
and taking the plurality of demand predicted values as input of an initial elastic network, taking the real demand in the samples in the verification sample set as expected output of the initial elastic network, and training the initial elastic network to obtain the elastic network.
4. The method of claim 1, wherein the demand interval parameter comprises an average demand interval; and
the step of determining whether the article to be predicted is an intermittent article according to the demand interval parameter of the article to be predicted comprises the following steps:
determining that the item to be predicted is an intermittent demand item in response to determining that the value of the average demand interval of the item to be predicted is greater than or equal to a preset demand interval threshold.
5. The method of claim 1, wherein the method further comprises:
and controlling replenishment equipment to replenish the to-be-predicted article according to the article demand.
6. An item demand generation device comprising:
the determining unit is configured to determine whether the to-be-predicted article is an intermittent demand article according to a demand interval parameter of the to-be-predicted article;
the acquisition unit is configured to respond to the fact that the to-be-predicted article is determined to be an intermittent demand article, acquire a pre-trained model pool and an elastic network, wherein models in the model pool are used for outputting predicted demand, and the elastic network is used for outputting article demand of the to-be-predicted article according to the predicted demand of each model in the model pool;
a generating unit configured to generate an item demand for the item to be forecasted using the model pool and the elastic network.
7. The apparatus of claim 6, wherein the obtaining unit is further configured to:
in response to determining that the article to be predicted is an intermittent demand article, determining a demand sparsity degree value of the article to be predicted;
and responding to the fact that the requirement sparsity degree value is larger than a preset sparsity degree threshold value, and obtaining a model pool and an elastic network which are trained in advance.
8. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
9. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-5.
10. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-5.
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