Efficient Proper Length Time Series Motif Discovery

As one of the most essential data mining tasks, finding frequently occurring patterns, i.e., motif discovery, has drawn a lot of attention in the past decade. Despite successes in speedup of motif discovery algorithms, most of the existing algorithms still require predefined parameters. The critical and most cumbersome one is time series motif length since it is difficult to manually determine the proper length of the motifs-even for the domain experts. In addition, with variability in the motif lengths, ranking among these motifs becomes another major problem. In this work, we propose a novel algorithm using compression ratio as a heuristic to discover meaningful motifs in proper lengths. The ranking of these various length motifs relies on an ability to compress time series by its own motif as a hypothesis. Furthermore, other than being an anytime algorithm, our experimental evaluation also demonstrates that our proposed method outperforms existing works in various domains both in terms of speed and accuracy.