Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting
arXiv:2606.01289v1 Announce Type: new Abstract: Zero-shot time series forecasting aims to predict future values for previously unseen series, requiring models to generalize temporal dynamics beyond the training distribution. While recent foundation models achieve strong in-domain performance through large-scale pretraining, their effectiveness often relies on broad data coverage and implicit pattern memorization, which can limit generalization when data are scarce or source and target domains are disjoint. In this work, we propose FSA, a feature-to-strategy framework for controlled zero-shot u
The proliferation of time series data across various domains demands more robust and generalizable forecasting methods, particularly as foundation models grow in capability but still face challenges with domain generalization.
This work directly addresses a critical limitation of current AI models – their difficulty in performing zero-shot forecasting on unseen time series data, which is essential for scaling AI applications in dynamic environments.
The proposed FSA framework provides a new strategy for improving the generalization capabilities of AI models for time series forecasting, potentially reducing the need for extensive domain-specific retraining.
- · AI model developers
- · Time series data analytics companies
- · Industries with novel or scarce time series data (e.g., biotech, advanced manufa
- · AI research community
- · Traditional, data-hungry forecasting models
- · Companies relying solely on in-domain, fine-tuned models for new tasks
Improved zero-shot forecasting capabilities will enable faster deployment of AI solutions in new and evolving domains.
Enhanced generalization could lead to more robust and less data-intensive AI development cycles, reducing compute and data collection costs.
The ability to forecast unseen temporal dynamics may accelerate scientific discovery and engineering innovation by uncovering patterns in novel data sets more efficiently.
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