A Comparative Analysis of Traditional Forecasting Methods and Machine Learning Techniques for Sales Prediction in E-commerce

Abstract

This paper presents a comparative analysis of traditional forecasting methods and machine learning (ML) techniques for sales prediction in e-commerce. We first review the literature on both traditional and ML methods for sales prediction in e-commerce, highlighting their strengths and weaknesses. The study uses a dataset of daily sales from an e-commerce retailer to conduct a comprehensive empirical study thar compares the performance of literature methods from both categories. The analysis considers different forecasting horizons and evaluates the accuracy of the predictions using various performance metrics, such as mean absolute error and mean squared error. The study finds that ML techniques generally outperform traditional methods, especially for longer forecasting horizons. However, some traditional methods, such as the Holt-Winters method, can also perform well under certain conditions. Our study provides insights into the relative strengths and weaknesses of traditional and ML methods for sales prediction in e-commerce and can guide practitioners in selecting appropriate methods for their specific requirements.