  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.
