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arXiv · 2103.00362

Robust Forecasting using Predictive Generalized Synchronization in Reservoir Computing

Jason A. Platt·Adrian S. Wong·Randall Clark·Stephen G. Penny·Henry D. I. Abarbanel
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Abstract

Reservoir computers (RC) are a form of recurrent neural network (RNN) used for forecasting timeseries data. As with all RNNs, selecting the hyperparameters presents a challenge when training onnew inputs. We present a method based on generalized synchronization (GS) that gives direction in designing and evaluating the architecture and hyperparameters of an RC. The 'auxiliary method' for detecting GS provides a computationally efficient pre-training test that guides hyperparameterselection. Furthermore, we provide a metric for RC using the reproduction of the input system's Lyapunov exponentsthat demonstrates robustness in prediction.

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Jason A. Platt, Adrian S. Wong, Randall Clark, Stephen G. Penny, Henry D. I. Abarbanel. 2021-02-28. Robust Forecasting using Predictive Generalized Synchronization in Reservoir Computing. https://doi.org/10.1063/5.0066013

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