E-Commerce Demand Forecasting Using SARIMA Model and K-means Clustering Analysis

Abstract

E-commerce has become a significant driver in the retail industry within the digital era, with escalating online transaction volumes. Accurate demand forecasting and effective inventory management are crucial for retailers to minimize costs related to overstock and stockouts while maximizing customer satisfaction and revenue. This study explores the application of advanced time series models for demand forecasting by e-commerce retailers, with a particular emphasis on the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The SARIMA model, an extension of the ARIMA model, incorporates autoregression, moving averages, and accounts for seasonal variations, making it suitable for analyzing data with regular periodic patterns, such as seasonal fluctuations in consumer purchasing behavior. The study methodology includes data preprocessing, establishment of the ARIMA model, construction of a Linear Regression (LR) model, development of the SARIMA model, demand forecasting, and clustering analysis. The SARIMA model demonstrated higher fit and predictive accuracy compared to traditional LR and ARIMA models, as substantiated by the 1-mWAPE and RMSE metrics. The application of the K-means clustering algorithm enhanced the homogeneity of demand forecasting and improved the model's adaptability to new sequence data. Cluster analysis categorized products into four classes, with more similar demand characteristics within each class, aiding the model in more accurately capturing potential demand changes. In conclusion, this research confirms the effectiveness and practicality of combining the SARIMA model with the K-means clustering algorithm for e-commerce demand forecasting.