Estimating Time Series Foundation Model Transferability via In-Context
  Learning
Time series foundation models (TSFMs) offer strong zero-shot forecasting via
large-scale pre-training, yet fine-tuning remains critical for boosting
performance in domains with limited public data. With the growing number of
TSFMs, efficiently identifying the best model for downstream fine-tuning
becomes increasingly challenging. In this work, we introduce TimeTic, a
transferability estimation framework that recasts model selection as an
in-context-learning problem: given observations on known (source) datasets, it
predicts how a TSFM will perform after fine-tuning on a downstream (target)
dataset. TimeTic flexibly organizes the observed model-data relationships as
contextual information, allowing it to adapt seamlessly to various test-time
scenarios. Leveraging the natural tabular structure formed by dataset
meta-features, model characteristics, and fine-tuned performance, we employ
tabular foundation models to serve as in-context learners. We further introduce
a novel model characterization based on entropy evolution across model layers,
capturing embedding-space distinctions and enabling TimeTic to generalize
across arbitrary model sets. We establish a comprehensive benchmark for
transferability estimation including 10 datasets, 10 foundation models, and 3
forecasting tasks. On this benchmark, TimeTic's estimation demonstrates strong
alignment with actual fine-tuned performance for previously unseen datasets,
achieving a mean rank correlation of approximately 0.6 and a 30% improvement
compared to using zero-shot performance as the transferability score.
68dbf49f4159d1f2418f980d
Qingren Yao
claimed_verified
2025-10-01T10:23:45.913Z
636480adf2984121733e503d
/avatars/bd5bcff68ca620f2ed05812abc65ac1f.svg
Qingren Yao
user
Qingren
68dbf49f4159d1f2418f980e
Ming Jin
68dbf49f4159d1f2418f980f
Chengqi Zhang
68dbf49f4159d1f2418f9810
Chao-Han Huck Yang
claimed_verified
2025-10-20T12:14:36.474Z
629e1b71bb6419817ed7566c
https://cdn-avatars.huggingface.co/v1/production/uploads/629e1b71bb6419817ed7566c/0ZCt-11eQtRDCOk9AozOp.jpeg
Huck Yang
user
huckiyang
68dbf49f4159d1f2418f9811
Jun Qi
68dbf49f4159d1f2418f9812
Shirui Pan
Estimating Time Series Foundation Model Transferability via In-Context
  Learning
Time series foundation models (TSFMs) offer strong zero-shot forecasting via
large-scale pre-training, yet fine-tuning remains critical for boosting
performance in domains with limited public data. With the growing number of
TSFMs, efficiently identifying the best model for downstream fine-tuning
becomes increasingly challenging. In this work, we introduce TimeTic, a
transferability estimation framework that recasts model selection as an
in-context-learning problem: given observations on known (source) datasets, it
predicts how a TSFM will perform after fine-tuning on a downstream (target)
dataset. TimeTic flexibly organizes the observed model-data relationships as
contextual information, allowing it to adapt seamlessly to various test-time
scenarios. Leveraging the natural tabular structure formed by dataset
meta-features, model characteristics, and fine-tuned performance, we employ
tabular foundation models to serve as in-context learners. We further introduce
a novel model characterization based on entropy evolution across model layers,
capturing embedding-space distinctions and enabling TimeTic to generalize
across arbitrary model sets. We establish a comprehensive benchmark for
transferability estimation including 10 datasets, 10 foundation models, and 3
forecasting tasks. On this benchmark, TimeTic's estimation demonstrates strong
alignment with actual fine-tuned performance for previously unseen datasets,
achieving a mean rank correlation of approximately 0.6 and a 30% improvement
compared to using zero-shot performance as the transferability score.
TimeTic is a transferability estimation framework that predicts the performance of time series foundation models after fine-tuning on unseen datasets, using tabular foundation models and entropy evolution for model characterization.
time series foundation models zero-shot forecasting fine-tuning transferability estimation in-context-learning contextual information tabular foundation models entropy evolution model characterization mean rank correlation