AI RESEARCH
REGAIN: REconciliation GAIN-driven Auxiliary Direction Learning
arXiv CS.LG
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ArXi:2606.04380v1 Announce Type: cross 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.