Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting
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 specialized SamFormer model across synthetic and real-world energy forecasting benchmarks. Our results show that while fine-tuning improves new task accuracy, it consistently triggers forgetting, though larger models exhibit greater inherent robustness. Notably, employing forgetting mitigation techniques
The rapid development and deployment of foundation models across various domains, including time series, necessitate understanding their practical limitations for long-term real-world applications.
This research provides critical insights into the challenge of catastrophic forgetting in continually fine-tuned foundation models, a key obstacle for deploying adaptive AI systems in dynamic environments.
The understanding of foundation model limitations in practical, adaptive scenarios is refined, emphasizing the need for robust forgetting mitigation techniques or specific architectural choices for continual learning.
- · AI researchers focusing on continual learning
- · Developers of robust time series forecasting platforms
- · Companies with highly dynamic data environments
- · Companies relying on naive fine-tuning of foundation models
- · Early adopters of unmitigated continual learning approaches
- · Generic 'one-size-fits-all' AI model providers
Further research and development will be directed towards effective catastrophic forgetting mitigation strategies for foundation models.
The adoption rates of continually learning AI systems may be temporarily tempered until more robust solutions emerge, shifting focus towards hybrid or specialized model retraining.
This could lead to a bifurcation in AI model development: highly adaptable but performance-constrained generalists vs. specialized, robust models with lower transferability but superior long-term stability.
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Read at arXiv cs.LG