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.
CITATION STYLE
Papatheodoulou, D., Pavlou, P., Vrachimis, S. G., Malialis, K., Eliades, D. G., & Theocharides, T. (2022). A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring. In IFIP Advances in Information and Communication Technology (Vol. 647 IFIP, pp. 53–66). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-08337-2_5
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