Time-Series Foundation Models That Understand Data Revisions
How time-series foundation models are evolving to handle data revisions and prevent historical prediction leakage.
Historical observations are not always fixed: statistical agencies revise previously published values as new evidence arrives. Forecasting from a contemporary download can therefore expose a model to information unavailable at the date it purportedly made a prediction. We propose...
ArXiv CS.LG
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This research addresses the critical problem of data leakage by ensuring time-series models account for historical revisions rather than relying on static, finalized datasets. By training models to handle shifting ground truths, developers can create more robust forecasting systems that accurately reflect the uncertainty and evolution of real-world statistics.
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