Ensemble Learning Models for Large-Scale Time Series Forecasting in Supply Chain

Machine learning techniques have gained significant traction in supply chain forecasting, driven by the increasing availability of data assets. These techniques offer opportunities to optimize management processes, reduce operational costs, and enhance decision-making for enterprise success. However, conventional statistical approaches dominating time series forecasting, such as the Autoregressive-moving-average model (ARMA), dynamic regression, and unobserved component models (UCMs), suffer from limitations in model accuracy and performance. They struggle to handle batch processing, large-scale big data, uncertainty-induced disruptions, and the synchronization of demand and supply scenarios. To address these challenges, we propose a class of ensemble techniques that combine neural networks with baseline models. Firstly, we conduct classification and segmentation by leveraging feature engineering on signal components, such as spikes and anomalies as outlier skews, to capture the complexity of combined scenarios in categorical data hierarchies and identify patterns for ensemble forecasting. Subsequently, we employ an ensemble model equipped with time series pattern sensors to automatically discern signal components, encompassing seasonality, promotions, trends, and intermittent or discontinued activities. We evaluate the performance of eight commonly-used model categories, and our proposed ensemble modeling approaches exhibit substantial improvements in accuracy compared to individual baseline models and other univariate time series algorithms.