Machine learning applications in time series hierarchical forecasting
This work addresses forecasting challenges in supply chain management, but it is incremental as it applies existing ML methods to a known bottleneck.
The paper tackled the problem of hierarchical forecasting for sales time series impacted by promotions by applying machine learning models to capture dynamic variations, and found that these models were competitive and outperformed some established methods on 61 groups of time series.
Hierarchical forecasting (HF) is needed in many situations in the supply chain (SC) because managers often need different levels of forecasts at different levels of SC to make a decision. Top-Down (TD), Bottom-Up (BU) and Optimal Combination (COM) are common HF models. These approaches are static and often ignore the dynamics of the series while disaggregating them. Consequently, they may fail to perform well if the investigated group of time series are subject to large changes such as during the periods of promotional sales. We address the HF problem of predicting real-world sales time series that are highly impacted by promotion. We use three machine learning (ML) models to capture sales variations over time. Artificial neural networks (ANN), extreme gradient boosting (XGboost), and support vector regression (SVR) algorithms are used to estimate the proportions of lower-level time series from the upper level. We perform an in-depth analysis of 61 groups of time series with different volatilities and show that ML models are competitive and outperform some well-established models in the literature.