Finding Motif Sets in Time Series
This work addresses the detection of repeated patterns in household electricity usage, which is an incremental improvement for energy management applications.
The paper tackled the problem of finding motif sets in time series, specifically for household electricity-usage profiles, by proposing three algorithms and showing that Scan MK is less accurate on synthetic data than Set Finder and Cluster MK, with the latter being sensitive to parameters, while both Scan MK and Set Finder discovered useful motif sets in real data.
Time-series motifs are representative subsequences that occur frequently in a time series; a motif set is the set of subsequences deemed to be instances of a given motif. We focus on finding motif sets. Our motivation is to detect motif sets in household electricity-usage profiles, representing repeated patterns of household usage. We propose three algorithms for finding motif sets. Two are greedy algorithms based on pairwise comparison, and the third uses a heuristic measure of set quality to find the motif set directly. We compare these algorithms on simulated datasets and on electricity-usage data. We show that Scan MK, the simplest way of using the best-matching pair to find motif sets, is less accurate on our synthetic data than Set Finder and Cluster MK, although the latter is very sensitive to parameter settings. We qualitatively analyse the outputs for the electricity-usage data and demonstrate that both Scan MK and Set Finder can discover useful motif sets in such data.