Tailored Forecasting from Short Time Series via Meta-learning

2025-01-27

AI Summary

To address low prediction accuracy and poor generalization across dynamical systems in short-term time series forecasting (<50 steps), this paper proposes a meta-learning-driven reservoir computing framework. Methodologically, it pioneers the integration of meta-learning with reservoir computing by constructing a multi-system long-horizon model library; for a target system, it adaptively retrieves a suitable base model and performs lightweight fine-tuning to yield a high-accuracy, system-specific predictor—simultaneously achieving accurate short-term trajectory forecasting and faithful preservation of long-term statistical properties. Key contributions include: (1) enabling cross-system dynamical transfer from extremely limited samples (e.g., <10 steps); and (2) demonstrating robustness to substantial dynamical disparities, validated on chaotic systems. Experiments show that our method significantly outperforms state-of-the-art baselines under data-scarce conditions, striking an effective balance between predictive accuracy and statistical consistency.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Machine learning (ML) models can be effective for forecasting the dynamics of unknown systems from time-series data, but they often require large amounts of data and struggle to generalize across systems with varying dynamics. Combined, these issues make forecasting from short time series particularly challenging. To address this problem, we introduce Meta-learning for Tailored Forecasting from Related Time Series (METAFORS), which uses related systems with longer time-series data to supplement limited data from the system of interest. By leveraging a library of models trained on related systems, METAFORS builds tailored models to forecast system evolution with limited data. Using a reservoir computing implementation and testing on simulated chaotic systems, we demonstrate METAFORS' ability to predict both short-term dynamics and long-term statistics, even when test and related systems exhibit significantly different behaviors and the available data are scarce, highlighting its robustness and versatility in data-limited scenarios.

Problem

• Limited Data
• Machine Learning
• Predictive Accuracy

Innovation

• METAFORS
• Data Augmentation
• Predictive Accuracy