Event-driven demand forecasting method for e-commerce based on prophet-random forest

Abstract

The invention discloses an event-driven demand forecasting method for e-commerce based on Prophet-random forest, comprising the steps of: acquiring historical sales data of an e-commerce platform, the sales data including time series data and user data for purchasing related products; cleaning the historical sales data , improve data quality; compress time series data to reduce data volatility; conduct event modeling based on festival activities and e-commerce platform promotion events; add event regressors to the Prophet model, and perform Prophet prediction, eliminate and interpolate as events Outliers within the effect range, and perform random forest prediction; combine Prophet prediction and random forest prediction results; perform accuracy evaluation to observe model generalization ability and prediction effect. Aiming at the characteristics of e-commerce event demand and non-event demand, the present invention uses Prophet and random forest for prediction respectively, and improves the prediction accuracy of the event-driven demand of e-commerce.

Description

technical field [0001] The invention belongs to the field of e-commerce demand forecasting, and in particular relates to an event-driven demand forecast method for e-commerce based on Prophet-random forest. Background technique [0002] The date interval between festival activities and e-commerce platform promotion events is not fixed, and does not show a standard cyclical movement law. The short-term consumer demand driven by it shows a surge in the short term of the event and a small fluctuation in the non-event effect period. state. However, the current commonly used method for e-commerce demand forecasting is the autoregressive moving average model. The premise of time series data forecasting is that the data is stable, and the law of unstable data cannot be well captured. Under the circumstance that platform promotion events are driven and have the characteristics of emergencies, the existing models cannot represent the needs of the real business environment. SUMMARY...

AI Technical Summary

Problems solved by technology

However, the current commonly used method for e-commerce demand forecasting is the autoregressive moving average model. The premise of its time-series data forecast is that the data is stable, and it cannot capture the law of unstable data well. When the platform promotion event is driven and has sudden characteristics, the existing model cannot reflect the demand in the real business environment

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