Motif Detection Inspired by Immune Memory (JORS)

TL;DR

This paper introduces the Motif Tracking Algorithm, an immune-inspired method capable of identifying variable-length, unknown motifs in time series data, demonstrating its effectiveness on industrial datasets.

Contribution

The paper presents a novel immune-inspired algorithm for pattern detection in time series, capable of identifying unknown, variable-length motifs independently of data specifics.

Findings

Successfully identified meaningful motifs in industrial datasets

Demonstrated flexibility and robustness of the algorithm

Discussed the value of detected motifs in practical contexts

Abstract

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.