A Comparison of Statistical and Machine Learning Approaches for Time Series Forecasting in a Demand Management Scenario

Abstract

The increasing size and complexity of datasets, the need for constant adaptation to current conditions, and the potential benefits of machine learning (ML) techniques, such as flexibility and the ability to incorporate additional features, have led to the increasing use of ML techniques in forecasting as an alternative to traditional statistical methods. However, the results are often not transferable to smaller datasets. This paper analyses a real inventory management dataset and compares statistical and ML methods to determine which techniques consistently produce accurate results, even for smaller datasets. The results show that the choice of aggregation level affects the performance of statistical and ML methods, with the LightGBM model showing consistent performance across different scenarios and aggregation levels, and simpler methods effectively modelling intermittent or lumpy time series.