REGAIN: REconciliation GAIN-driven Auxiliary Direction Learning
Forecast reconciliation usually starts from a fixed measurement system and asks how forecasts should be projected onto a coherent space. We ask a different question: which additional linear measurements should be forecast and included in the reconciliation system? We propose REGAIN, a reconciliation-gain framework that learns normalized auxiliary directions, forecasts the induced series with a frozen forecasting oracle, and selects directions by their target-weighted loss reduction after augmented generalized least-squares reconciliation. Unlike variance-based components or predictability-based auxiliary selection, REGAIN optimizes the downstream effect of an auxiliary measurement on the ...
| Authors | Weijia Li, Shun Hu, Yanfei Kang |
|---|---|
| Primary category | stat.ML |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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