Time series motif discovery is an increasingly popular research area in time series mining whose main objective is to search for interesting patterns or motifs. A motif is a pair of time series subsequences, or two subsequences whose shapes are very similar to each other. Typical motif discovery algorithm requires a predefined motif length as its parameter. Discovering motif with arbitrary lengths introduces another problem, where selecting a suitable length for the motif is non-trivial since domain knowledge is often required. Only a few works were aware of this motif length and proposed some algorithms to resolve the problem. However, these algorithms still require an initial motif length parameter and many additional pre-defined parameters which cause a lot more complication for using and especially the motif length parameter is still remain. Thus, this work proposes the first parameter-free motif discovery algorithm which requires no parameter as input, and as a result returns a set of all “Best Motif” that are ranked by a proposed scoring function which is based on similarity of motif locations and similarity of motif shapes. The experimental results show that the algorithm can efficiently discover all planted patterns with high quality and are able to reduce a number of all possible motifs with more than 99 percent.
