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arXiv · 2501.16325

Tailored Forecasting from Short Time Series via Meta-learning

Declan A. Norton·Edward Ott·Andrew Pomerance·Brian Hunt·Michelle Girvan
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Abstract

Machine learning models can effectively forecast dynamical systems from time-series data, but they typically require large amounts of past data, making forecasting particularly challenging for systems with limited history. To overcome this, we introduce Meta-learning for Tailored Forecasting using Related Time Series (METAFORS), which generalizes knowledge across systems to enable forecasting in data-limited scenarios. By learning from a library of models trained on longer time series from potentially related systems, METAFORS builds and initializes a model tailored to short time-series data from the system of interest. Using a reservoir computing implementation and testing on simulated chaotic systems, we demonstrate that METAFORS can reliably predict both short-term dynamics and long-term statistics without requiring contextual labels. We see this even when test and related systems exhibit substantially different behaviors, highlighting METAFORS' strengths in data-limited scenarios.

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Declan A. Norton, Edward Ott, Andrew Pomerance, Brian Hunt, Michelle Girvan. 2025-07-31. Tailored Forecasting from Short Time Series via Meta-learning. https://doi.org/10.1126/sciadv.ady7216

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