Motif detection inspired by immune memory

The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the motif tracking algorithm (MTA), a novel immune-inspired pattern identification tool that is able to identify variable length unknown motifs that repeat within time series data. The algorithm searches from a neutral perspective that is independent of the data being analysed and the underlying motifs. In this paper we test the flexibility of the MTA by applying it to the search for patterns in two industrial data sets. The algorithm is able to identify a population of meaningful motifs in both cases, and the value of these motifs is discussed.