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arXiv · 2403.19806

Feature-Based Echo-State Networks: A Step Towards Interpretability and Minimalism in Reservoir Computer

Debdipta Goswami
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Abstract

This paper proposes a novel and interpretable recurrent neural-network structure using the echo-state network (ESN) paradigm for time-series prediction. While the traditional ESNs perform well for dynamical systems prediction, it needs a large dynamic reservoir with increased computational complexity. It also lacks interpretability to discern contributions from different input combinations to the output. Here, a systematic reservoir architecture is developed using smaller parallel reservoirs driven by different input combinations, known as features, and then they are nonlinearly combined to produce the output. The resultant feature-based ESN (Feat-ESN) outperforms the traditional single-reservoir ESN with less reservoir nodes. The predictive capability of the proposed architecture is demonstrated on three systems: two synthetic datasets from chaotic dynamical systems and a set of real-time traffic data.

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Debdipta Goswami. 2024-03-28. Feature-Based Echo-State Networks: A Step Towards Interpretability and Minimalism in Reservoir Computer. https://arxiv.org/abs/2403.19806

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