Go to Computo homepageReservoir Computing in R: a Tutorial for Using reservoirnet to Predict Complex Time-Series
Abstract: 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.
Repository Url: https://github.com/thomasferte/reservoirnet_computo
Changes Since Last Submission: Dear Editors and Reviewers,
Thank you for your thoughtful comments and suggestions. We have
carefully considered all feedback and provided a point-by-point response
to each reviewer. The revised PDF has been submitted, and the updated
HTML version is available here:
<https://thomasferte.github.io/reservoirnet_computo/>.\
The corresponding GitHub repository with the updated code can be found
at: <https://github.com/thomasferte/reservoirnet_computo>.
Best regards,
Thomas Ferté
Assigned Action Editor: ~Julien_Chiquet1
Submission Number: 16
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