Motif detection inspired by immune memory
提出一种受免疫系统启发的模式追踪算法，能从时间序列数据中自动识别长度未知的重复模式，并在两个工业数据集上验证了其有效性。
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.