Reservoir Computing in R: a Tutorial for Using reservoirnet to Predict Complex Time-Series

Reservoir Computing (RC) is a machine learning method based on neural networks that efficiently process information generated by dynamical systems. It has been successful in solving various tasks including time series forecasting, language processing or voice processing. RC is implemented in `Python` and `Julia` but not in `R`. This article introduces `reservoirnet`, an `R` package providing access to the `Python` API `ReservoirPy`, allowing `R` users to harness the power of reservoir computing. This article provides an introduction to the fundamentals of RC and showcases its real-world applicability through three distinct sections. First, we cover the foundational concepts of RC, setting the stage for understanding its capabilities. Next, we delve into the practical usage of `reservoirnet` through two illustrative examples. These examples demonstrate how it can be applied to real-world problems, specifically, regression of COVID-19 hospitalizations and classification of Japanese vowels. Finally, we present a comprehensive analysis of a real-world application of `reservoirnet`, where it was used to forecast COVID-19 hospitalizations at Bordeaux University Hospital using public data and electronic health records.