arXiv:2602.11550v2 Announce Type: replace-cross Abstract: Time Series Foundation Models (TSFMs) achieve strong zero-shot forecasting through large-scale pre-training, but adapting them to downstream domains under distribution shift remains challenging. Existing solutions face a trade-off: Parametric Adaptation can cause catastrophic forgetting and requires costly multi-domain maintenance, while Non-Parametric Retrieval improves forecasts but incurs high inference latency due to datastore search. We propose Parametric Memory Distillation and implement it as TS-Memory, a lightweight memory adapt
The proliferation of Time Series Foundation Models (TSFMs) has highlighted the critical challenge of adapting these powerful but general models to specific, dynamically changing domains without sacrificing performance or efficiency.
This development addresses a key limitation in the practical deployment of TSFMs, enabling more robust and efficient forecasting in critical applications, and pushing the frontier of AI model usability.
The ability to adapt TSFMs without catastrophic forgetting or high inference latency means these models can be more readily applied to diverse real-world time series problems, expanding their utility and impact.
- · AI/ML developers
- · Industries relying on time series forecasting
- · Cloud AI providers
- · Enterprise AI
- · Inefficient domain-specific modeling approaches
- · Traditional non-parametric retrieval methods due to latency
Improved accuracy and efficiency in time series forecasting across numerous applications.
Accelerated adoption of foundation models in specialized domains due to easier adaptation methods.
New AI-driven services and products built upon highly adaptable time series predictions, potentially impacting sectors like finance, logistics, and energy management.
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Read at arXiv cs.AI