Centrality-Based Pruning for Efficient Echo State Networks
This work addresses efficiency issues in ESNs for researchers and practitioners in time-series forecasting, though it is incremental as it builds on existing pruning techniques.
The paper tackles the problem of redundant nodes in Echo State Networks (ESNs) for time-series prediction, proposing a graph centrality-based pruning method that reduces reservoir size by up to 50% while maintaining or improving accuracy.
Echo State Networks (ESNs) are a reservoir computing framework widely used for nonlinear time-series prediction. However, despite their effectiveness, the randomly initialized reservoir often contains redundant nodes, leading to unnecessary computational overhead and reduced efficiency. In this work, we propose a graph centrality-based pruning approach that interprets the reservoir as a weighted directed graph and removes structurally less important nodes using centrality measures. Experiments on Mackey-Glass time-series prediction and electric load forecasting demonstrate that the proposed method can significantly reduce reservoir size while maintaining, and in some cases improving, prediction accuracy, while preserving the essential reservoir dynamics.