Local vs. Global Models for Hierarchical Forecasting
This work addresses forecasting challenges in domains with hierarchical data, offering incremental improvements in accuracy and efficiency for decision-making.
The study tackled hierarchical time series forecasting by comparing local models with Global Forecasting Models (GFMs), finding that GFMs based on LightGBM provided more accurate and computationally efficient solutions, with superior accuracy and lower complexity than local models and conventional methods like Exponential Smoothing and ARIMA.
Hierarchical time series forecasting plays a crucial role in decision-making in various domains while presenting significant challenges for modelling as they involve multiple levels of aggregation, constraints, and availability of information. This study explores the influence of distinct information utilisation on the accuracy of hierarchical forecasts, proposing and evaluating locals and a range of Global Forecasting Models (GFMs). In contrast to local models, which forecast each series independently, we develop GFMs to exploit cross-series and cross-hierarchies information, improving both forecasting performance and computational efficiency. We employ reconciliation methods to ensure coherency in forecasts and use the Mean Absolute Scaled Error (MASE) and Multiple Comparisons with the Best (MCB) tests to assess statistical significance. The findings indicate that GFMs possess significant advantages for hierarchical forecasting, providing more accurate and computationally efficient solutions across different levels in a hierarchy. Two specific GFMs based on LightGBM are introduced, demonstrating superior accuracy and lower model complexity than their counterpart local models and conventional methods such as Exponential Smoothing (ES) and Autoregressive Integrated Moving Average (ARIMA).