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arXiv · 2210.00089

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

Dimitris Papatheodoulou·Pavlos Pavlou·Stelios G. Vrachimis·Kleanthis Malialis·Demetrios G. Eliades·Theocharis Theocharides
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Abstract

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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Dimitris Papatheodoulou, Pavlos Pavlou, Stelios G. Vrachimis, Kleanthis Malialis, Demetrios G. Eliades, Theocharis Theocharides. 2022-09-30. A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring. https://doi.org/10.1007/978-3-031-08337-2_5

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