Merchant passenger flow volume method and apparatus

Abstract

The present invention provides a method and device for predicting business traffic, comprising: S100 acquiring user consumption data and feature attribute information affecting customer traffic; S200 constructing a decision tree model respectively according to the user consumption data and the feature attribute information And the target ARIMA model to predict the future traffic of each merchant. The invention organically combines the characteristics and advantages of the decision tree model and the ARIMA model, provides a scientific and accurate model selection method, and more accurately predicts the customer flow of merchants.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a method and device for predicting customer flow of a merchant. Background technique [0002] With the popularity of mobile location services, large e-commerce platforms have accumulated massive online and offline transaction data from users and merchants. The merchant platform uses these data to provide merchants with customized back-end business intelligence services including transaction statistics, sales analysis and sales recommendations. For example, an e-commerce platform can provide sales forecasts for each merchant. Based on the prediction results, merchants can optimize operations, reduce costs, and improve user experience. [0003] Under normal circumstances, forecasting is generally aimed at relatively large and complex systems such as traffic flow and weather. For the forecasting of business passenger flow, there is little research at home and abroad. Generally, simp...

AI Technical Summary

Problems solved by technology

[0003] Under normal circumstances, forecasting is generally aimed at relatively large and complex systems such as traffic flow and weather. For the forecasting of business passenger flow, there is little research at home and abroad. Generally, simple historical data statistics are used to predict using some time series methods. There is often a large gap between the predicted results of such methods and the actual results.

Since there are many factors that affect passenger flow, this method of predicting passenger flow based on historical data will lead to low prediction accuracy, especially short-term prediction accuracy.

To sum up, the accuracy of the passenger flow predicted by the method for predicting the passenger flow of merchants in the prior art is relatively low

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