Study of E-commerce Sale Prediction Based on Machine Learning Methods

Rong, Liu and Annie, Joseph and Wanzhen, Wang and Feng, Cao and Na, Li and Taihao, Zhang (2025) Study of E-commerce Sale Prediction Based on Machine Learning Methods. Journal of Advanced Research Design, 145 (1). pp. 136-148. ISSN 2289-7984

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Abstract

Precise overall sales forecasting is essential in the sales domain for controlling slow-moving commodities and cutting inventory expenses. However, seasonality, trends, and multi-product scenarios provide challenges for established methods of sales forecasting. For time series data and complicated patterns, models such as gated cycle unit (GRU), recurrent neural network (RNN), and short-and long-termmemory network (LSTM) were chosen to increase processing power. To find the best models for sales forecasting, the performance of these models was compared using metrics (MAE, RMSE, and R!). It is found that GRU model is the best model in this field. In order to assure the research's suitability from a scientific and practical standpoint, these additional components have been added to increase the study's scope, address the issue of previous research using these models sparingly or not at all, and lookfor more efficient ways to forecast sales.

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