Thomas Ferté, Kalidou Ba, Dan Dutartre, Pierrick Legrand, Vianney Jouhet, Rodolphe Thiébaut, Xavier Hinaut, Boris P Hejblum 2025-06-27
Thomas Ferté, Kalidou Ba, Dan Dutartre, Pierrick Legrand, Vianney Jouhet, Rodolphe Thiébaut, Xavier Hinaut and Boris P Hejblum (June 2025). Reservoir Computing in R: a Tutorial for Using reservoirnet to Predict Complex Time-Series. Computo. https://doi.org/10.57750/arxn-6z34
- Thomas Ferté (Inserm, Inria, CHU de Bordeaux)
- Kalidou Ba (Inserm, Inria)
- Dan Dutartre (Inria)
- Pierrick Legrand (Bordeaux INP, Inria, IMS)
- Vianney Jouhet (Inserm, CHU de Bordeaux)
- Rodolphe Thiébaut (Inserm, Inria, CHU de Bordeaux)
- Xavier Hinaut (Inria, IMN, LaBRI)
- Boris P Hejblum (Inserm, Inria)
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.