Parameter-free motif discovery for time series data
Pawan Nunthanid, Vit Niennattrakul, Chotirat Ann Ratanamahatana · 2012
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. Thus, this work proposes a parameter-free motif discovery algorithm called k-Best Motif Discovery (kBMD) which requires no parameter as input, and as a result returns a set of all “Best Motif” that are ranked by our proposed scoring function which is based on similarity of motif locations and similarity of motif shapes.