HINT: Hierarchical Coherent Networks For Constrained Probabilistic Forecasting

Large collections of time series data are commonly organized into hierarchies with different levels of aggregation.We present Hierarchical Coherent Networks (HINT), a forecasting framework that adheres to these aggregation constraints. We specialized HINT in the task via a multivariate mixture optimized with composite likelihood and made coherent via bootstrap reconciliation. Additionally, we robustify the networks to stark time series scale variations, incorporating normalized feature extraction and recomposition of output scales within their architecture. We demonstrate improved accuracy compared to the existing state-of-the-art. We provide ablation studies on our model's components and its solid theoretical foundations. HINT's code is available at this http URL.