arXiv Machine Learning
Event Signature Transfer: Model-Agnostic Forecast Scenario Construction from Historical Events
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When, Not How Much: Evaluating Time-Series Foundation Models on Sparse Events
arXiv:2609.39386v1 Announce Type: new Abstract: Pretrained time-series foundation models (TSFMs) are evaluated as forecasters of future values, yet for sparse series many decisions depend only on whi...
arXiv Machine Learning
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FlowTSFM: Turning Encoder Depth into Quantile Transport
arXiv:2609.13640v1 Announce Type: new Abstract: Encoder-based time series foundation models (TSFMs) typically rely on deep stacks of independently parameterized Transformer layers, where only the fin...
arXiv Machine Learning
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From Recoverability to Functional Use: Certifying Temporal Reports in Time-Series Forecasting
arXiv:2608. 10433v2 Announce Type: replace Abstract: Temporal reports are increasingly emitted alongside numerical forecasts and are often interpreted as statements about the computation producing those forecasts.
arXiv AI
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MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting
arXiv:2606. 28670v1 Announce Type: cross Abstract: We introduce MACROCAST, a lightweight Time Series Foundation Model (TSFM) for real-time macroeconomic forecasting.
arXiv Machine Learning
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Crossing-Free Probabilistic K-Line Forecasts Without Retraining
arXiv:2607. 26792v1 Announce Type: cross Abstract: Probabilistic K-line forecasting describes uncertainty in four complementary prices, namely open--high--low--close (OHLC).
arXiv Machine Learning
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Tabby: An Open Pretraining Recipe for Time Series Foundation Models
arXiv:2609.13956v1 Announce Type: new Abstract: In this report, we release Tabby, a long context probabilistic time series foundation model, together with a complete and open recipe of how it was bui...