Recent advances in mining time series data

Authors:
Eamonn Keogh
Affiliations:
Department of Computer Science & Engineering, University of California, Riverside
Venue:
ECML'05 Proceedings of the 16th European conference on Machine Learning
Year:
2005

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Abstract

Much of the world's supply of data is in the form of time series. Furthermore, as we shall see, many types of data can be meaningfully converted into ”time series”, including text, DNA, video, images etc. The last decade has seen an explosion of interest in mining time series data from the academic community. There has been significant work on algorithms to classify, cluster, segment, index, discover rules, visualize, and detect anomalies/novelties in time series. In this talk I will summarize the latest advances in mining time series data, including: – New representations of time series data. – New algorithms/definitions. – The migration from static problems to online problems. – New areas and applications of time series data mining. I will end the talk with a discussion of “what's left to do” in time series data mining.