Tailored forecasting from short time series via meta-learning

Area of Science:

• Complex Systems
• Machine Learning
• Time Series Analysis

Background:

• Machine learning models excel at forecasting dynamical systems but require extensive historical data.
• Forecasting is difficult for systems with limited time-series data.
• Generalizing knowledge across systems is crucial for data-limited scenarios.

Purpose of the Study:

• Introduce a novel meta-learning framework, METAFORS (meta-learning for tailored forecasting using related time series).
• Enable accurate forecasting for dynamical systems with limited historical data.
• Demonstrate generalization of knowledge across related systems.

Main Methods:

• Developed METAFORS, a meta-learning approach that leverages knowledge from related time series.
• Utilized a reservoir computing implementation for forecasting.
• Tested the framework on simulated chaotic systems with varying behaviors.

Main Results:

• METAFORS successfully forecasts short-term dynamics and long-term statistics from limited time-series data.
• The model generalizes effectively even when test and related systems exhibit substantially different behaviors.
• No contextual labels were required for successful forecasting.

Conclusions:

• METAFORS offers a robust solution for forecasting dynamical systems in data-limited environments.
• The framework demonstrates significant strengths in generalizing learned patterns across diverse systems.
• This approach advances the capability of machine learning in scientific forecasting applications.