Reservoir Computing for Short High-Dimensional Time Series: an Application to SARS-CoV-2 Hospitalization Forecast
| dc.rights.license | open | en_US |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | FERTE, Thomas | |
| hal.structure.identifier | Service Expérimentation et Développement [Bordeaux] [SED] | |
| dc.contributor.author | DUTARTRE, Dan | |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | HEJBLUM, Boris
ORCID: 0000-0003-0646-452X IDREF: 189970316 | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | GRIFFIER, Romain
IDREF: 252908562 | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | JOUHET, Vianney | |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | THIEBAUT, Rodolphe | |
| hal.structure.identifier | Méthodes avancées d’apprentissage statistique et de contrôle [ASTRAL] | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | LEGRAND, Pierrick
IDREF: 094614032 | |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| hal.structure.identifier | Mnemonic Synergy [Mnemosyne] | |
| hal.structure.identifier | Institut des Maladies Neurodégénératives [Bordeaux] [IMN] | |
| dc.contributor.author | HINAUT, Xavier
IDREF: 22823218X | |
| dc.contributor.editor | SALAKHUTDINOV, Ruslan | |
| dc.date.accessioned | 2024-09-23T12:50:00Z | |
| dc.date.available | 2024-09-23T12:50:00Z | |
| dc.date.issued | 2024-07-08 | |
| dc.date.conference | 2024-07-21 | |
| dc.identifier.issn | 1938-7228 | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/201744 | |
| dc.description.abstractEn | In this work, we aimed at forecasting the number of SARS-CoV-2 hospitalized patients at 14 days to help anticipate the bed requirements of a large scale hospital using public data and electronic health records data. Previous attempts ledto mitigated performance in this high-dimension setting; we introduce a novel approach to time series forecasting by providing an alternative to conventional methods to deal with high number of potential features of interest (409 predictors). We integrate Reservoir Computing (RC) with feature selection using a genetic algorithm (GA) to gatheroptimal non-linear combinations of inputs to improve prediction in sample-efficient context. We illustrate that the RC-GA combination exhibitsexcellent performance in forecasting SARS-CoV-2 hospitalizations. This approach outperformed the use of RC alone and other conventional methods: LSTM, Transformers, Elastic-Net, XGBoost. Notably, this work marks the pioneering use of RC (along with GA) in the realm of short and high-dimensional time series, positioning it as a competitive and innovative approach in comparison to standard methods. | |
| dc.language.iso | EN | en_US |
| dc.title.en | Reservoir Computing for Short High-Dimensional Time Series: an Application to SARS-CoV-2 Hospitalization Forecast | |
| dc.type | Communication dans un congrès | en_US |
| dc.subject.hal | Statistiques [stat]/Machine Learning [stat.ML] | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| bordeaux.volume | 235 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.institution | INRIA | en_US |
| bordeaux.conference.title | Proceedings of the 41 st International Conference on Machine Learning | en_US |
| bordeaux.country | at | en_US |
| bordeaux.title.proceeding | International Conference on Machine Learning, 21-27 July 2024, Vienna, Austria | en_US |
| bordeaux.team | SISTM_BPH | en_US |
| bordeaux.team | AHEAD_BPH | en_US |
| bordeaux.conference.city | Vienne | en_US |
| bordeaux.import.source | hal | |
| hal.identifier | hal-04693930 | |
| hal.version | 1 | |
| hal.proceedings | oui | en_US |
| hal.conference.end | 2024-07-27 | |
| hal.popular | non | en_US |
| hal.audience | Internationale | en_US |
| hal.export | false | |
| workflow.import.source | hal | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2024-07-08&rft.volume=235&rft.eissn=1938-7228&rft.issn=1938-7228&rft.au=FERTE,%20Thomas&DUTARTRE,%20Dan&HEJBLUM,%20Boris&GRIFFIER,%20Romain&JOUHET,%20Vianney&rft.genre=unknown |
