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Communication Dans Un Congrès Année : 2022

A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring

Dimitris Papatheodoulou
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  • PersonId : 1406748
Pavlos Pavlou
  • Fonction : Auteur
  • PersonId : 1406749
Stelios G. Vrachimis
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  • PersonId : 1406750
Kleanthis Malialis
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Demetrios G. Eliades
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  • PersonId : 1406752
Theocharis Theocharides
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  • PersonId : 1406753

Résumé

Numerous real-world problems from a diverse set of application areas exist that exhibit temporal dependencies. We focus on a specific type of time series classification which we refer to as aggregated time series classification. We consider an aggregated sequence of a multi-variate time series, and propose a methodology to make predictions based solely on the aggregated information. As a case study, we apply our methodology to the challenging problem of household water end-use dissagregation when using non-intrusive water monitoring. Our methodology does not require a-priori identification of events, and to our knowledge, it is considered for the first time. We conduct an extensive experimental study using a residential water-use simulator, involving different machine learning classifiers, multi-label classification methods, and successfully demonstrate the effectiveness of our methodology.
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Dates et versions

hal-04668660 , version 1 (07-08-2024)

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Dimitris Papatheodoulou, Pavlos Pavlou, Stelios G. Vrachimis, Kleanthis Malialis, Demetrios G. Eliades, et al.. A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.53-66, ⟨10.1007/978-3-031-08337-2_5⟩. ⟨hal-04668660⟩
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