Motif Detection Inspired by Immune Memory (JORS)
This work addresses pattern detection for researchers in fields like industrial data analysis, but it appears incremental as it builds on immune-inspired methods without claiming major breakthroughs.
The paper tackled the problem of identifying variable-length unknown motifs in time series data by proposing the Motif Tracking Algorithm, an immune-inspired tool, and demonstrated its flexibility by successfully identifying meaningful motifs in two industrial datasets.
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, a novel immune inspired pattern identification tool that is able to identify variable length unknown motifs which 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 motif tracking algorithm 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.