New store sales volume prediction method, apparatus and device, and storage medium

By acquiring and preprocessing the sales data of the target area and training the time series prediction model, the problem of insufficient data volume and interpretability in the sales forecast of new stores is solved, and automated prediction and higher accuracy are achieved.

CN119740710AActive Publication Date: 2025-04-01创优数字科技(广东)有限公司
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Patent Information

Application Number
CN202411969553.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-30
Publication Date
2025-04-01
Estimated Expiration
2044-12-30

AI Technical Summary

Technical Problem

There are limitations in the front-end data volume and data interpretability in the sales forecasting process of new store in the prior art, and the prediction effect depends on personnel experience.

Method used

By obtaining the first sales data of the target area within the preset period, including the sales data of multiple existing stores and target stores, performing data preprocessing such as data cleaning and data splicing, setting the target parameters and parameter search range of the time series prediction model, and training the model to predict the sales data of the target store.

Benefits of technology

It improves the data volume and effectiveness of new store sales forecasts, realizes automated forecasts, reduces dependence on personnel experience, and improves the accuracy and practicality of forecasts.

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Abstract

The invention provides a new store sales volume prediction method and device, equipment and a storage medium. The first sales data of the target area in the preset time period is acquired, so that all sales conditions of the new store after opening the business and the sales conditions of the existing store in a certain time period are acquired, the sales data of the existing store and the target store are jointly used as basic data of sales volume prediction, namely the first sales data, and further, the sales volume prediction efficiency is improved. And performing a preprocessing task of data cleaning and data splicing according to the first sales data to obtain second sales data, thereby training a time series prediction model of a preset search parameter condition, obtaining a time series prediction model under an optimal parameter combination, and further predicting and obtaining third sales data of the target shop. Therefore, the front-end data volume and the data validity are both improved, and automatic new store sales volume prediction is realized.
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