Abstract: Existing variable-length motif discovery algorithms have the problems that its speed is slow, its scalability is poor, and its results include meaningless phantoms such as too short, too long, or ordinary matching. A time series variable-length motif mining algorithm based on Matrix Profile is proposed. The algorithm uses the STOMP algorithm as a subroutine, and uses the lower bound distance combined with the incremental calculation to accelerate the process of extracting candidate motifs. The length similarity condition and the equivalent class method of motif group are used to remove the meaningless motifs that are too short, too long, or trival matched. Experiments on the dataset UCR show that proposed algorithm can effectively filter the meaningless motifs when the variable-length motifs are found, and has high efficiency and accuracy.

Key words: time series, motif mining, Matrix Profile, variable-length motif