Robust Forecasting using Predictive Generalized Synchronization in Reservoir Computing

TL;DR

This paper introduces a generalized synchronization-based method for designing and evaluating reservoir computers, improving robustness and efficiency in time series forecasting by guiding hyperparameter selection and assessing system stability.

Contribution

It presents a novel GS-based approach for hyperparameter tuning and robustness assessment in reservoir computing, enhancing forecasting performance.

Findings

GS-based pre-training guides hyperparameter selection effectively.

Lyapunov exponent reproduction indicates model robustness.

Method improves forecasting accuracy and stability.

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.