BWhat changed
Pretrained a patched decoder on a broad time-series corpus to support forecasting across varying input histories, output horizons and temporal granularities without task-specific retraining.
Scientific scope
Pretrained a patched decoder on a broad time-series corpus to support forecasting across varying input histories, output horizons and temporal granularities without task-specific retraining. Forecasting benchmarks do not prove universal transfer or uncertainty calibration. Original report is2023; public releases and later TimesFM2.x architectural/features are not backdated.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Forecasting benchmarks do not prove universal transfer or uncertainty calibration. Original report is2023; public releases and later TimesFM2.x architectural/features are not backdated.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
- A decoder-only foundation model for time-series forecasting
- Das et al.: TimesFM patched-decoder time-series foundation model — author/institution implementation
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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