Cross-temporal forecast reconciliation using machine learning

· Abstract: Many forecasting tasks involve multiple, interrelated time series that must satisfy linear aggregation constraints, where the components collectively sum to the total. Ensuring such coherence across all aggregation levels is the goal of forecast reconciliation, which is essential for consistent and aligned decision-making. In cross-temporal frameworks, the focus of this talk, these aggregation constraints extend across both cross-sectional and temporal dimensions. Existing literature primarily relies on linear reconciliation methods, which adjust base forecasts through linear transformations within a least-squares framework to satisfy aggregation constraints. In this work, we move beyond this paradigm and introduce a non-linear forecast reconciliation approach for cross-temporal frameworks. Our method directly and automatically produces cross-temporal coherent forecasts by leveraging popular machine learning techniques. We empirically validate our framework on large-scale streaming datasets from a leading European on-demand delivery platform and a bicycle-sharing system in New York City.

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