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Sales prediction and neural network construction method and device, equipment and storage medium

A prediction method and neural network technology, applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve the problems of inaccurate prediction effect and low effectiveness, achieve optimal model representation ability, improve accuracy, optimize The effect of representational ability

Pending Publication Date: 2020-09-11
创新奇智(南京)科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, the sales forecast of products is mainly based on experience to extract features of information such as sales, inventory, price, products, stores, etc., and use it as the input of the model for training. The prediction effect obtained in this way is inaccurate and has low effectiveness.

Method used

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  • Sales prediction and neural network construction method and device, equipment and storage medium
  • Sales prediction and neural network construction method and device, equipment and storage medium
  • Sales prediction and neural network construction method and device, equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0057] see figure 1 , figure 1 It is a schematic flowchart of a sales forecasting method disclosed in the embodiment of this application. Such as figure 1 As shown, the method includes the steps of:

[0058] 101. Obtain historical sales data of the target commodity within at least one time window and current product information of the target commodity;

[0059] 102. Use the neural network model to perform hollow convolution processing on the historical sales data and extract the trend features of the historical sales data. The trend features represent the degree of influence of the product information of the target product on the historical sales volume of the target product;

[0060] 103. According to the trend characteristics and using the neural network model to train the current product information and obtain the sales forecast data of the target commodity.

[0061] Exemplarily, past 56 daily sales data of the target commodity may be obtained as historical sales data. ...

Embodiment 2

[0068] see figure 2 , figure 2 It is a schematic flowchart of a sales forecasting method disclosed in the embodiment of this application. Such as figure 2 As shown, the method includes the steps of:

[0069] 201. Obtain the historical sales data of the target commodity in at least one time window and the current product information of the target commodity;

[0070] 202. Using the neural network model to perform hollow convolution processing on the historical sales data and extract the trend features of the historical sales data, the trend features represent the degree of influence of the product information of the target product on the historical sales volume of the target product;

[0071] 203. Correct the loss function of neural network model training according to the following formula:

[0072]

[0073] Among them, a is the weight coefficient of (0,1), n ​​is the number of training samples, and y i is the target variable of the i-th training sample, y i-t Be the...

Embodiment 3

[0077] see image 3 , image 3 It is a schematic flowchart of a method for constructing a neural network disclosed in the embodiment of the present application, and the method is applied to a sales forecasting method. Such as image 3 As shown, the method includes the steps of:

[0078] 301. Build an input layer, the input layer is used to obtain the historical sales data of the target commodity in at least one time window and the current product information of the target commodity;

[0079] 302. Construct a convolutional layer, which is used to perform dilated convolution processing on historical sales data and extract trend features of historical sales data, where trend features represent the degree of influence of product information of the target product on the historical sales volume of the target product;

[0080] 303. Construct a fully connected layer and a hidden layer, which are used to train current product information according to trend features and obtain traini...

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Abstract

The invention discloses a sales prediction and neural network construction method and device, equipment and a storage medium, and the method comprises the steps: obtaining the historical sales data ofa target commodity in at least one time window and the current product information of the target commodity; performing hole convolution processing on the historical sales data by using a neural network model, and extracting trend features of the historical sales data, wherein the trend features represent the influence degree of the commodity information of the target commodity on the historical sales volume of the target commodity; and training the current product information by using the neural network model according to the trend characteristics, and obtaining sales prediction data of the target commodity. According to the method, tThe subsequent sales volume of the commodity can be accurately and effectively predicted by obtaining the historical sales data of the commodity, and meanwhile, the neural network of the method has better model characterization capability.

Description

technical field [0001] The present application relates to the retail field, and in particular to a method, device, device, and storage medium for sales forecasting and neural network construction. Background technique [0002] At present, in the retail industry, sales forecast is a very core link, and its sales forecast results are used to optimize promotional pricing, inventory management, production scheduling and other tasks. However, due to the influence of seasonality, more than 95% of the products that change seasons are new products and the product validity period is 13 weeks, and affected by fashion factors, the sales of products show different trends. For example, the main products are stable and explosive. A steady, rapidly declining trend. Therefore, it is necessary to quickly predict the sales volume of the product. [0003] At present, product sales forecast is mainly based on experience to extract features of sales, inventory, price, product, store and other ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06N3/04G06N3/08
CPCG06Q30/0202G06N3/08G06N3/045
Inventor 周鹏程杨路飞
Owner 创新奇智(南京)科技有限公司
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