A method and system for processing store sales data

By evaluating the accuracy and sample coverage of decision trees, dynamically allocating weight factors, and optimizing the random forest algorithm, the prediction accuracy and applicability issues of traditional random forest models in store sales forecasting are resolved, resulting in more accurate and reliable prediction results and supporting enterprises in optimizing operations.

CN121685004BActive Publication Date: 2026-08-28GUANGDONG WINSHANG NETWORK DATA SERVICE CO LTD
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Patent Information

Application Number
CN202511732989.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-11-24
Publication Date
2026-08-28
Estimated Expiration
2045-11-24

AI Technical Summary

Technical Problem

Traditional random forest models suffer from varying prediction accuracy in store sales forecasting due to the equal-weighted averaging strategy, and different decision trees have different applicability to the samples, leading to prediction results that deviate from the actual values.

Method used

By evaluating the accuracy, sample coverage, and feature matching of each decision tree, weight factors are dynamically allocated, and the prediction model of the random forest algorithm is optimized by combining the path homology of the samples to be predicted.

Benefits of technology

It improves the accuracy and reliability of store sales forecasting, enhances the model's adaptability to complex scenarios, reduces forecast bias, and supports enterprises in more scientific inventory management and marketing strategies.

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Abstract

This invention relates to the field of image data processing technology, and more particularly to a method and system for processing store sales data. The method includes: acquiring reference samples and actual sales figures for each store, as well as samples to be predicted for which store sales figures need to be predicted; training a random forest algorithm and using the trained model for prediction; designating any prediction tree in the trained model as the target tree and determining the accuracy of the target tree; determining the weight factors for the samples to be predicted when using the target tree for prediction; and determining the final predicted sales figures for the samples to be predicted, thereby achieving store sales data processing. This invention dynamically allocates weight factors for each tree by comprehensively considering multiple dimensions such as the training sample coverage, prediction error of unselected samples, and sample path homogeneity of a single decision tree, avoiding interference from low-quality predictions on the final result and improving the accuracy and reliability of store sales prediction.
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