arXiv:2606.04380v1 Announce Type: cross Abstract: 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. Unlik
The proliferation of complex data systems and the increasing demand for accurate, coherent forecasts in various domains necessitate advanced reconciliation techniques.
Improved forecasting methods, particularly in managing interconnected forecast systems, can significantly enhance operational efficiency, resource allocation, and strategic planning across industries.
This new framework shifts the focus from simply projecting forecasts onto a coherent space to intelligently selecting and integrating additional data inputs for reconciliation, potentially leading to more robust predictive models.
- · Businesses reliant on complex forecasting models
- · Data scientists and machine learning researchers
- · Sectors with high data interconnectivity (e.g., finance, logistics)
- · Organizations using simplistic or outdated reconciliation methods
More accurate and reliable forecasts become achievable for systems with intricate dependencies.
Enhanced forecasting capabilities could lead to optimized supply chains, better financial risk management, and improved resource planning.
Organizations that effectively implement such advanced reconciliation frameworks could gain a competitive advantage through superior predictive power and operational agility.
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Read at arXiv cs.LG