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.
This work has been supported by the European Union Horizon 2020 program under Grant Agreement No. 739551 (TEAMING KIOS CoE) and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy, and the FLOBIT project co-funded by the Research and Innovation Foundation of Cyprus, the European Regional Development Fund and Structural Funds of the European Union in Cyprus.
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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: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-08337-2_5
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DOI: https://doi.org/10.1007/978-3-031-08337-2_5
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