Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Cluster and Calendar Based Visualization of Time Series Data
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing and discovering non-trivial patterns in large time series databases
Information Visualization
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Price trackers inspired by immune memory
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Detecting motifs in system call sequences
WISA'07 Proceedings of the 8th international conference on Information security applications
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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 completely 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 motifs successfully in both cases, and the value of these motifs is discussed.