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.
Highlights
The investigation and analysis of time series data is a popular and well studied area of research
This provides an ideal opportunity for an Artificial Immune System (AIS) driven approach to tackle the problem of motif detection, as a distinguishing feature of the Motif Tracking Algorithm (MTA) is its ability to identify variable length unknown patterns that repeat in a time series
The MTA is compared to the probabilistic motif detection algorithm developed by Keogh
Summary
The investigation and analysis of time series data is a popular and well studied area of research. Common goals of time series analysis include the desire to identify known patterns in a time series, to predict future trends given historical information and the ability to classify data into similar clusters. The power of the MTA comes from the fact that it requires no prior knowledge of the time series to be examined or what motifs exist It searches in a fast and efficient manner and the flexibility incorporated in its generic approach allows the MTA to be applied across a diverse range of problems. In contrast little research has been performed on looking for unknown motifs in time series This provides an ideal opportunity for an AIS driven approach to tackle the problem of motif detection, as a distinguishing feature of the MTA is its ability to identify variable length unknown patterns that repeat in a time series.
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