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Deep learning based retail commodity sales forecasting method

A technology of deep learning and forecasting method, which is applied in the field of retail product sales forecasting based on deep learning, can solve the problems of low calculation efficiency and poor accuracy, and achieve the effect of improving accuracy and good application value

Inactive Publication Date: 2017-09-19
ZHEJIANG GONGSHANG UNIVERSITY
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AI Technical Summary

Problems solved by technology

[0005] The present invention overcomes the shortcomings of poor accuracy and low calculation efficiency of existing retail commodity sales forecasting methods, and proposes a retail commodity sales forecasting method based on deep learning

Method used

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  • Deep learning based retail commodity sales forecasting method
  • Deep learning based retail commodity sales forecasting method
  • Deep learning based retail commodity sales forecasting method

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Embodiment Construction

[0019] The present invention will be further described in detail in conjunction with the accompanying drawings and specific embodiments.

[0020] The retail commodity sales prediction method based on deep learning that the present invention proposes comprises the following steps:

[0021] 1) Data preprocessing: process the missing data in the acquired data set. First, analyze and organize the data dimensions, delete the dimensions that have no effect on the classification and prediction results; then clean up the data corresponding to the remaining data dimensions, quantify unstructured data, and supplement missing values, such as some that obey the normal distribution The data can be filled with the mean value.

[0022] 2) Build the base classifier of random forest: random forest is a combination of Bagging algorithm and Random Subspace algorithm, and the basic constituent unit is decision tree, which is the base classifier. Suppose the set D={(x i1 ,x i2 ,...,x iM ,y i...

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Abstract

The invention relates to a deep learning based retail commodity sales forecasting method, which comprises the steps of 1, performing data preprocessing; 2, building a base classifier of the random forest; 3, randomly selecting a feature subset; and 4, forecasting the sales trend of retail commodities. According to the invention, a problem of accurate sales forecasting for retail commodities is studied, influences imposed on a sales result by various nonlinear factors are dug out, a defect that part of nonlinear models are easy to fall into local minimum and slow in convergence speed is avoided at the same time, and enterprises are helped to perform efficient and accurate sales trend forecasting. An integrated classifier sales forecasting model of the random forest is built based on deep learning. The accuracy of sales forecasting is improved scientifically and reasonably according to the method.

Description

technical field [0001] The invention includes artificial intelligence and data mining technical field knowledge, and specifically relates to a method for predicting retail product sales based on deep learning. Applicable to all kinds of retail enterprises to achieve accurate sales forecast and improve the performance of enterprises. technical background [0002] With the rapid development of the Internet, traditional industries are under tremendous pressure and challenges, and offline retailing has been continuously impacted by online retailing. Online shopping has gradually become the main way for people to buy goods, and the categories of goods range from large home appliances and furniture to household items, snacks and drinks. Many e-commerce companies divide various subdivisions according to the consumption behavior of users and the characteristics of products, and carry out refined sales. The traditional offline physical store consumption model is being transformed a...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06F17/30G06K9/62
CPCG06F16/90335G06Q30/0202G06F18/214G06F18/2415G06F18/24323
Inventor 肖亮王璐雅许翀寰
Owner ZHEJIANG GONGSHANG UNIVERSITY
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