{"ID":2862048,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00809","arxiv_id":"2510.00809","title":"Are Time Series Foundation Models Susceptible to Catastrophic Forgetting?","abstract":"Time Series Foundation Models (TSFMs) have shown promising zero-shot generalization across diverse forecasting tasks. However, their robustness to continual adaptation remains underexplored. In this work, we investigate the extent to which TSFMs suffer from catastrophic forgetting when fine-tuned sequentially on multiple datasets. Using synthetic datasets designed with varying degrees of periodic structure, we measure the trade-off between adaptation to new data and retention of prior knowledge. Our experiments reveal that, while fine-tuning improves performance on new tasks, it often causes significant degradation on previously learned ones, illustrating a fundamental stability-plasticity dilemma.","short_abstract":"Time Series Foundation Models (TSFMs) have shown promising zero-shot generalization across diverse forecasting tasks. However, their robustness to continual adaptation remains underexplored. In this work, we investigate the extent to which TSFMs suffer from catastrophic forgetting when fine-tuned sequentially on multip...","url_abs":"https://arxiv.org/abs/2510.00809","url_pdf":"https://arxiv.org/pdf/2510.00809v2","authors":"[\"Nouha Karaouli\",\"Denis Coquenet\",\"Elisa Fromont\",\"Martial Mermillod\",\"Marina Reyboz\"]","published":"2025-10-01T12:14:34Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}