Discovery of variable length time series motif

One significant task in time series mining research area is motif discovery which is the first step needed to be done in finding interesting patterns in time series sequence. Recently, many motif discovery algorithms have been proposed in place of the untenable brute-force algorithm, to improve its time complexity. However, those motif discovery algorithms still need a predefined sliding window length that must be known a priori. In this paper, we present a novel motif discovery algorithm that requires no window length parameter. This sliding window length is sensitive in that a small difference in the value can lead to huge difference of motif results. The proposed algorithm automatically returns suitable motif lengths from all possible sliding window lengths; in other words, our algorithm efficiently reduces a large set of possibilities of the sliding window lengths down to a few truly-interesting variable-length motifs.