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arXiv · 2102.08930

Forecasting Using Reservoir Computing: The Role of Generalized Synchronization

Jason A. Platt·Adrian 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 time series data. As with all RNNs, selecting the hyperparameters presents a challenge when training on new inputs. We present a method based on generalized synchronization (GS) that gives direction in designing and evaluating the architecture and hyperparameters of a RC. The 'auxiliary method' for detecting GS provides a pre-training test that guides hyperparameter selection. Furthermore, we provide a metric for a "well trained" RC using the reproduction of the input system's Lyapunov exponents.

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Jason A. Platt, Adrian Wong, Randall Clark, Stephen G. Penny, Henry D. I. Abarbanel. 2021-02-04. Forecasting Using Reservoir Computing: The Role of Generalized Synchronization. https://arxiv.org/abs/2102.08930

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