A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data
Daniel Canaday,
Andrew Pomerance,
Michelle Girvan
Abstract:Recent research has established the effectiveness of machine learning for data-driven prediction of the future evolution of unknown dynamical systems, including chaotic systems. However, these approaches require large amounts of measured time series data from the process to be predicted. When only limited data is available, forecasters are forced to impose significant model structure that may or may not accurately represent the process of interest. In this work, we present a Meta-learning Approach to Reservoir… Show more
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