Merchant passenger flow prediction method fusing historical mean value and boosted tree

Abstract

The invention relates to a merchant passenger flow prediction method fusing a historical mean value and a boosted tree. The method is characterized by comprising the following steps of preprocessing merchant complete behavior data in a time period; constructing features for the preprocessed data; based on the historical mean value and the boosted tree, building a passenger flow prediction model; and performing passenger flow prediction. The invention provides an internet merchant passenger flow prediction model fusing the historical mean value and the boosted tree. The essence of the model isa weighted sum after weight coefficients, calculated by a calculation formula, of a boosted tree model and a historical mean value model are fused according to a certain ratio. The method considers not only how to improve the prediction precision of the model but also a dependency relationship between passenger flow prediction and time, and performs comparative analysis on prediction results of different models.

Description

technical field [0001] The invention relates to a passenger flow forecasting model that integrates historical mean values ​​and boosted trees, and belongs to the fields of intelligent information processing and machine learning. Background technique [0002] The development of mobile positioning services has led to a sharp increase in the "online and offline" transaction data of Internet merchants. Compared with the traditional retail industry, the marketing of Internet merchants pays more attention to user consumption, and is committed to bringing users a better consumption experience in terms of product details page introduction, customer service, and convenient mobile payment. For example, some business intelligence service platforms can provide sales forecasts for each merchant. Based on the prediction results, merchants can establish a trust relationship with users, attract more loyal users, optimize operational decisions, reduce costs, and improve user experience. ...

AI Technical Summary

Problems solved by technology

However, in real life, users' consumption behavior is often affected by factors such as holidays and weather. At this time, the existing technology cannot predict the customer flow of the merchant in time, which may lead to unsatisfactory prediction accuracy. Largely deviates from the actual customer flow of the merchant

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