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Post-Training in Time Series Foundation Models: A Unifying Framework
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Research introduces a unifying framework for post-training methods in time series foundation models (TSFMs) to improve downstream deployment.
OneBench interpretation
Institutional assessment
So what
Effective post-training strategies are critical for deploying generalized time series foundation models across diverse banking use cases without extensive retraining.
Do what
This framework offers a structured approach to evaluating adaptation techniques for internal time series models, influencing future architecture decisions for fraud, risk, and trading systems.