Commodity sales prediction method and device, computer equipment and storage medium

Abstract

The invention relates to a commodity sales prediction method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a first commodity data set of a target commodity in a preset time period, wherein the first commodity data set comprises the sales of the target commodity and the time data corresponding to the sales; according to the time data corresponding to the sales volume, performing time sorting on the sales volume in the first commodity data set according to a time sequence to obtain a second commodity data set; obtaining a target time sequencemodel according to the second commodity data set, wherein the target time sequence model is used for representing a corresponding relationship between the sales volume of the commodity and the time data; and inputting the target time data into the target time sequence model to obtain an output value of the target time sequence model, the output value being a predicted sales volume of the target category of commodities corresponding to the target time data. According to the method, the commodity sales volume can be automatically predicted so as to improve the commodity sales volume prediction efficiency.

Description

technical field [0001] The present application relates to the technical field of data processing, in particular to a method, device, computer equipment and storage medium for forecasting commodity sales. Background technique [0002] In recent years, smart retail and other new retail industries have developed rapidly, and the physical stores of large retail companies are growing at an extremely fast rate. The formulation of store sales plans is particularly important. Due to the large number of product categories and the large differences in store location, level and positioning, it is necessary to reasonably allocate the store sales plan amount for precise marketing and reasonable allocation of resources. At present, the company will have a special planning and budget department at the beginning of the year to formulate the general monthly sales plan of the store for the next year, but the daily sales plan in the monthly sales plan does not distinguish between product categ...

AI Technical Summary

Problems solved by technology

In particular, the sales amount daily plan also needs to predict the future sales based on the past sales of each category of goods for planning, and use manual experience to predict future sales based on the past sales of each category of goods for manual allocation of the sales amount daily plan. Not only lead to heavy workload and low efficiency, but also the accuracy of distribution results is not high

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