Evaluation of Time Series Forecasting Strategies for Demand Management

Time series forecasting allows businesses to gain insights into market trends and plan production, storage, and sales in advance. There is a shift towards applying machine learning (ML) models for forecasting on hierarchical sales datasets. However, large historical training data is not always available, particularly in markets where sales are not frequent but rather occur weekly or monthly. In such cases, current ML approaches often require years of training data. This study focuses on small datasets and evaluates the potential of ensemble methods to improve prediction accuracy. Forecasting models are assessed on three hierarchical sales datasets from two companies. Sixteen traditional statistical and ML models were evaluated using customized test periods for each dataset. The results show that the best-performing model depends on the dataset's characteristics. A seasonal naive model showed effective seasonality detection, while a baseline model robustly outperformed complex models in one of the datasets. Among traditional statistical models, theta and exponential smoothing produced similar results, and a statistical ensemble outperformed in larger prediction horizons. ML models showed mixed results, with ML ensembles showing significant advantages on the largest dataset. The study demonstrates the importance of model choice relative to dataset specifics, with traditional statistical approaches often outperforming their machine-learning counterparts on small datasets.