{"id":51512916,"url":"https://github.com/computorg/published-202505-ferte-reservoirnet","last_synced_at":"2026-07-08T08:02:27.573Z","repository":{"id":295818612,"uuid":"990542910","full_name":"computorg/published-202505-ferte-reservoirnet","owner":"computorg","description":"Reservoir Computing in R: a Tutorial for Using reservoirnet to Predict Complex Time-Series","archived":false,"fork":false,"pushed_at":"2025-07-29T07:49:18.000Z","size":21641,"stargazers_count":2,"open_issues_count":2,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-07-29T09:43:36.871Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"http://computo-journal.org/published-202505-ferte-reservoirnet/","language":"TeX","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc-by-4.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/computorg.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-05-26T09:18:59.000Z","updated_at":"2025-07-29T07:49:21.000Z","dependencies_parsed_at":"2025-06-25T14:41:42.489Z","dependency_job_id":null,"html_url":"https://github.com/computorg/published-202505-ferte-reservoirnet","commit_stats":null,"previous_names":["computorg/published-202505-ferte-reservoirnet"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/computorg/published-202505-ferte-reservoirnet","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202505-ferte-reservoirnet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202505-ferte-reservoirnet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202505-ferte-reservoirnet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202505-ferte-reservoirnet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/computorg","download_url":"https://codeload.github.com/computorg/published-202505-ferte-reservoirnet/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202505-ferte-reservoirnet/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35257142,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-08T02:00:06.796Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-07-08T08:02:26.729Z","updated_at":"2026-07-08T08:02:27.558Z","avatar_url":"https://github.com/computorg.png","language":"TeX","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Reservoir Computing in R: a Tutorial for Using reservoirnet to Predict Complex Time-Series\nThomas Ferté, Kalidou Ba, Dan Dutartre, Pierrick Legrand, Vianney\nJouhet, Rodolphe Thiébaut, Xavier Hinaut, Boris P Hejblum\n2025-06-27\n\n### Citation\n\nThomas 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.\n\u003chttps://doi.org/10.57750/arxn-6z34\u003e\n\n### Badges\n\n[![build and\npublish](https://github.com/computorg/published-202505-ferte-reservoirnet/actions/workflows/build.yml/badge.svg)](https://github.com/computorg/published-202505-ferte-reservoirnet/actions/workflows/build.yml)\n[![reviews](https://img.shields.io/badge/review-report-blue)](https://github.com/computorg/published-202505-ferte-reservoirnet/issues?q=is%3Aopen+is%3Aissue+label%3Areview)\n[![SWH](https://archive.softwareheritage.org/badge/origin/https://github.com/computorg/published-202505-ferte-reservoirnet)](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/computorg/published-202505-ferte-reservoirnet)\n[![DOI:10.57750/arxn-6z34](https://img.shields.io/badge/DOI-10.57750%2Farxn--6z34-034E79.svg)](https://doi.org/10.57750/arxn-6z34)\n[![Creative Commons\nLicense](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)\n\n### Authors’ affiliations\n\n- Thomas Ferté (Inserm, Inria, CHU de Bordeaux)\n- Kalidou Ba (Inserm, Inria)\n- Dan Dutartre (Inria)\n- Pierrick Legrand (Bordeaux INP, Inria, IMS)\n- Vianney Jouhet (Inserm, CHU de Bordeaux)\n- Rodolphe Thiébaut (Inserm, Inria, CHU de Bordeaux)\n- Xavier Hinaut (Inria, IMN, LaBRI)\n- Boris P Hejblum (Inserm, Inria)\n\n### Abstract\n\nReservoir Computing (RC) is a machine learning method based on neural\nnetworks that efficiently process information generated by dynamical\nsystems. It has been successful in solving various tasks including time\nseries forecasting, language processing or voice processing. RC is\nimplemented in `Python` and `Julia` but not in `R`. This article\nintroduces `reservoirnet`, an `R` package providing access to the\n`Python` API `ReservoirPy`, allowing `R` users to harness the power of\nreservoir computing. This article provides an introduction to the\nfundamentals of RC and showcases its real-world applicability through\nthree distinct sections. First, we cover the foundational concepts of\nRC, setting the stage for understanding its capabilities. Next, we delve\ninto the practical usage of `reservoirnet` through two illustrative\nexamples. These examples demonstrate how it can be applied to real-world\nproblems, specifically, regression of COVID-19 hospitalizations and\nclassification of Japanese vowels. Finally, we present a comprehensive\nanalysis of a real-world application of `reservoirnet`, where it was\nused to forecast COVID-19 hospitalizations at Bordeaux University\nHospital using public data and electronic health records.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomputorg%2Fpublished-202505-ferte-reservoirnet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcomputorg%2Fpublished-202505-ferte-reservoirnet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomputorg%2Fpublished-202505-ferte-reservoirnet/lists"}