Adaptive Reservoir Computing for Multi-Scenario Chaotic System Forecasting

• URL: http://arxiv.org/abs/2605.28145v1
• Date: Wed, 27 May 2026 08:27:02 GMT
• Title: Adaptive Reservoir Computing for Multi-Scenario Chaotic System Forecasting
• Abstract summary: We present an adaptive reservoir computing framework for the CTF-4-Science Lorenz benchmark.<n>We tailor the training and prediction procedure of Echo State Networks (ESNs) to the specific demands of each evaluation scenario.<n>The proposed framework achieves a score of 74.91 on the public benchmark leaderboard.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an adaptive reservoir computing framework for the CTF-4-Science Lorenz benchmark, which evaluates machine learning models across twelve distinct tasks spanning five qualitatively different scenarios: baseline forecasting, noisy signal reconstruction, forecasting under noise, few-shot learning, and parametric generalization. Rather than applying a uniform inference strategy, we tailor the training and prediction procedure of Echo State Networks (ESNs) to the specific demands of each evaluation scenario. Our key contributions are fourfold: (1) exact reservoir state synchronization that eliminates warmup approximation error in short-time prediction; (2) histogram-guided candidate selection that directly optimizes the long-time ergodic evaluation metric; (3) multi-seed reservoir search for few-shot regimes with severely limited training data; and (4) sequential multi-sequence training that resolves state-distribution mismatch in parametric generalization tasks. The proposed framework achieves a score of 74.91 on the public benchmark leaderboard, demonstrating that carefully adapted reservoir computing constitutes a competitive and computationally efficient approach for diverse chaotic system modeling challenges.

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