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

TL;DR

This paper introduces a novel multi-label time series classification method for aggregated data, applied to non-intrusive household water end-use monitoring, demonstrating effectiveness without requiring event identification.

Contribution

It presents a new methodology for aggregated time series classification applied to water monitoring, without needing prior event detection, validated through extensive experiments.

Findings

Effective classification of water end-use without event detection

Method outperforms baseline approaches in accuracy

Applicable to other aggregated time series problems

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.