Research on E-Commerce Commodity Demand Forecasting and Inventory Optimization Based on Time Series Forecasting Model

Abstract

In this study, 1996 sets of time series data are extracted from the three dimensions of merchant, warehouse and commodity, and the outliers are removed using the 3σ criterion. Through visual analysis, it was found that home improvement building materials and food and beverages were popular commodities. Then ARIMA and LSTM models were used to forecast the demand. Through 1-wmape index evaluation, LSTM model was found to perform better. Finally, the K-Means cluster analysis is carried out based on the categories of goods, merchants and warehouses, and the cluster number is selected as 70. The cosine similarity method is used to match the newly added dimensions to the category of the cluster in problem 1, and the ARIMA model is used for prediction. The results show that the cosine similarity method can accurately predict the commodity demand, the 1-wmape index is 0.801, and the residual plot presents randomness. The data of November 2022 is added to the original data, and the missing data is processed by linear interpolation. The LSTM model was used to forecast the increase of the total demand during Double 11. By setting a periodic time series with a step size of 30, the model achieves a good accuracy in predicting the demand in June, with a 1-wmape index of 0.829. To sum up, this study comprehensively considers three dimensions of merchants, warehouses and commodities, and uses different models to make accurate predictions. The ARIMA and LSTM models are adopted, while the promotion characteristics are considered and the appropriate LSTM parameters are selected. The research results provide practical forecasting tools and methods for supply chain management.