A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring
This addresses a domain-specific challenge in non-intrusive water monitoring for households, presenting an incremental approach to multi-label time series classification.
The paper tackles the problem of household water end-use disaggregation from aggregated multivariate time series data, proposing a methodology that does not require prior event identification and demonstrates effectiveness through experiments with a residential water-use simulator.
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.