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News/Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting
arxiv
PublishedJune 29, 2026 at 4:00 AM
—neutral

Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2510.00809v3 Announce Type: replace Abstract: While Time Series Foundation Models (TSFMs) excel in zero-shot tasks, their behavior under continual fine tuning is poorly understood. We present the first systematic study of catastrophic forgetting in TSFMs (TimesFM-2.0, Chronos-2) versus a speci

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Discussion
Mentioned models
03
  • 01
    TimesFM-2.0
  • 02
    Chronos-2
  • 03
    SamFormer
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#time-series#continual-learning#catastrophic-forgetting#benchmark

No replies yet. Be first.

Mentioned models
03
  • 01
    TimesFM-2.0
  • 02
    Chronos-2
  • 03
    SamFormer
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#time-series#continual-learning#catastrophic-forgetting#benchmark

Related coverage

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arxivVeriEvolve: Evaluator-First Evolution for Tool-Using Agents1darxivTNODEV: Toolbox for Neural ODE Verification1darxivLearning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding1darxivOn the Expressive Power and Limitations of Multi-Layer SSMs1d
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