TSDATAMODEL: LEARNING FORECASTING UTILITY FOR EXTERNAL CONTEXT RETRIEVAL WITH FROZEN TIME-SERIES FOUNDATION MODELS

Time-series foundation models can forecast new domains with frozen weights, but short observed histories may leave their predictions ambiguous. An external archive can supply additional histories, yet conventional retrieval asks which history looks similar to a target query rather than which one actually improves its forecast. We propose TSDataModel, which learns this missing quantity–forecasting utility. For a labeled fitting query, utility is the reduction in forecasting loss obtained by prepending a source history to the query. We estimate this utility with global, reference-based, and parametric scorers. Auto-TSDM chooses among them using a chronologically held-out selection set and freezes the choice before testing; test futures are never used for retrieval or model selection, and the forecasting backbone is never updated. We evaluate six target datasets with TimesFM 2.5-200M and TimeMoE-50M, a disjoint archive built from three source datasets, five random seeds, and length-matched context controls. Auto-TSDM improves over short-history target-only forecasting in 11 of 12 target–backbone settings, reducing mean MAE by 9.3% on average, and outperforms the strongest non-utility retrieval result in mean MAE in 11 of 12 settings. These results position forecasting-utility retrieval as a practical weight-update-free alternative when longer target histories or weight access are unavailable, rather than as a universal replacement for either.