Are Time Series Foundation Models Susceptible to Catastrophic Forgetting?

• URL: http://arxiv.org/abs/2510.00809v2
• Date: Thu, 02 Oct 2025 09:08:29 GMT
• Title: Are Time Series Foundation Models Susceptible to Catastrophic Forgetting?
• Authors: Nouha Karaouli, Denis Coquenet, Elisa Fromont, Martial Mermillod, Marina Reyboz,
• Abstract summary: Time Series Foundation Models (TSFMs) have shown promising zero-shot generalization across diverse forecasting tasks.<n>We investigate the extent to which TSFMs suffer from catastrophic forgetting when fine-tuned sequentially on multiple datasets.
• Score: 2.692427265051276
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models [51.506429027626005]
  Memory for Time Series (MEMTS) is a lightweight and plug-and-play method for retrieval-free domain adaptation in time series forecasting.<n>Key component of MEMTS is a Knowledge Persistence Module (KPM), which internalizes domain-specific temporal dynamics.<n>This paradigm shift enables MEMTS to achieve accurate domain adaptation with constant-time inference and near-zero latency.
  arXiv  Detail & Related papers  (2026-02-14T14:00:06Z)
• Contextual and Seasonal LSTMs for Time Series Anomaly Detection [49.50689313712684]
  We propose a novel prediction-based framework named Contextual and Seasonal LSTMs (CS-LSTMs)<n>CS-LSTMs are built upon a noise decomposition strategy and jointly leverage contextual dependencies and seasonal patterns.<n>They consistently outperform state-of-the-art methods, highlighting their effectiveness and practical value in robust time series anomaly detection.
  arXiv  Detail & Related papers  (2026-02-10T11:46:15Z)
• From Observations to States: Latent Time Series Forecasting [65.98504021691666]
  We propose Latent Time Series Forecasting (LatentTSF), a novel paradigm that shifts TSF from observation regression to latent state prediction.<n>Specifically, LatentTSF employs an AutoEncoder to project observations at each time step into a higher-dimensional latent state space.<n>Our proposed latent objectives implicitly maximize mutual information between predicted latent states and ground-truth states and observations.
  arXiv  Detail & Related papers  (2026-01-30T20:39:44Z)
• Time Series Foundation Models for Process Model Forecasting [8.339024524110828]
  Process Model Forecasting aims to predict how the control-flow structure of a process evolves over time.<n>Machine learning and deep learning models provide only modest gains over statistical baselines.<n>We investigate Time Series Foundation Models (TSFMs) as an alternative for PMF.
  arXiv  Detail & Related papers  (2025-12-08T15:08:50Z)
• Synapse: Adaptive Arbitration of Complementary Expertise in Time Series Foundational Models [50.877082340479085]
  We study how different Time Series Foundational Models (TSFMs) exhibit specialized performance profiles across various forecasting settings.<n>We propose Synapse, a novel arbitration framework for TSFMs.<n>Results demonstrate that Synapse consistently outperforms other popular ensembling techniques as well as individual TSFMs.
  arXiv  Detail & Related papers  (2025-11-07T18:01:51Z)
• A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting [81.73338008264115]
  Current approaches for time series forecasting, whether in the time or frequency domain, predominantly use deep learning models based on linear layers or transformers.<n>We propose FIRE, a unified frequency domain decomposition framework that provides a mathematical abstraction for diverse types of time series.<n>Fire consistently outperforms state-of-the-art models on long-term forecasting benchmarks.
  arXiv  Detail & Related papers  (2025-10-11T09:59:25Z)
• Estimating Time Series Foundation Model Transferability via In-Context Learning [74.65355820906355]
  Time series foundation models (TSFMs) offer strong zero-shot forecasting via large-scale pre-training.<n>Fine-tuning remains critical for boosting performance in domains with limited public data.<n>We introduce TimeTic, a transferability estimation framework that recasts model selection as an in-context-learning problem.
  arXiv  Detail & Related papers  (2025-09-28T07:07:13Z)
• Multi-Scale Finetuning for Encoder-based Time Series Foundation Models [56.503053716053]
  Time series foundation models (TSFMs) demonstrate impressive zero-shot performance for time series forecasting.<n>We argue that it falls short of fully leveraging TSFMs' capabilities, often resulting in overfitting and suboptimal performance.<n>We propose textbftextscfinetextbftextsctuning (textbfMSFT), a simple yet general framework that explicitly integrates multi-scale modeling into the finetuning process.
  arXiv  Detail & Related papers  (2025-06-17T01:06:01Z)
• Less is More: Unlocking Specialization of Time Series Foundation Models via Structured Pruning [27.23328609888911]
  Time Series Foundation Models pre-train vast parameters and achieve remarkable zero-shot forecasting performance.<n>Surprisingly, even after fine-tuning, TSFMs cannot consistently outperform smaller, specialized models trained on full-shot downstream data.<n>We propose a structured pruning method to regularize the subsequent fine-tuning process by focusing it on a more relevant and compact parameter space.
  arXiv  Detail & Related papers  (2025-05-29T07:33:49Z)
• Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains [23.9530536685668]
  Time Series Foundation Models (TSFMs) are pretrained on large-scale, cross-domain data and capable of zero-shot forecasting in new scenarios without further training.<n>Are TSFMs robust to adversarial input perturbations?<n>These perturbations could be exploited in man-in-the-middle attacks or data poisoning.
  arXiv  Detail & Related papers  (2025-05-26T01:24:11Z)
• Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models [104.17057231661371]
  Time series analysis is crucial for understanding dynamics of complex systems.<n>Recent advances in foundation models have led to task-agnostic Time Series Foundation Models (TSFMs) and Large Language Model-based Time Series Models (TSLLMs)<n>Their success depends on large, diverse, and high-quality datasets, which are challenging to build due to regulatory, diversity, quality, and quantity constraints.<n>This survey provides a comprehensive review of synthetic data for TSFMs and TSLLMs, analyzing data generation strategies, their role in model pretraining, fine-tuning, and evaluation, and identifying future research directions.
  arXiv  Detail & Related papers  (2025-03-14T13:53:46Z)
• Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation [39.7344214193566]
  We introduce a pioneering test-time adaptation framework tailored for time series forecasting (TSF)<n>TAFAS, the proposed approach to TSF-TTA, flexibly adapts source forecasters to continuously shifting test distributions while preserving the core semantic information learned during pre-training.<n>The novel utilization of partially-observed ground truth and gated calibration module enables proactive, robust, and model-agnostic adaptation of source forecasters.
  arXiv  Detail & Related papers  (2025-01-09T04:59:15Z)
• Evaluating Time Series Foundation Models on Noisy Periodic Time Series [0.0]
  This paper presents an empirical study evaluating the performance of time series foundation models (TSFMs) over two datasets constituting noisy periodic time series.<n>Our findings demonstrate that while for time series with bounded periods, TSFMs can match or outperform the statistical approaches, their forecasting abilities deteriorate with longer periods, higher noise levels, lower sampling rates and more complex shapes of the time series.
  arXiv  Detail & Related papers  (2025-01-01T16:36:21Z)
• Large Continual Instruction Assistant [59.585544987096974]
  Continual Instruction Tuning (CIT) is adopted to instruct Large Models to follow human intent data by data.<n>Existing update gradient would heavily destroy the performance on previous datasets during CIT process.<n>We propose a general continual instruction tuning framework to address the challenge.
  arXiv  Detail & Related papers  (2024-10-08T11:24:59Z)
• Fast-Slow Test-Time Adaptation for Online Vision-and-Language Navigation [67.18144414660681]
  We propose a Fast-Slow Test-Time Adaptation (FSTTA) approach for online Vision-and-Language Navigation (VLN) Our method obtains impressive performance gains on four popular benchmarks.
  arXiv  Detail & Related papers  (2023-11-22T07:47:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.