Introducing time series snippets: a new primitive for summarizing long time series

• Autores: Shima Imani, Frank Madrid, Wei Ding, Scott E. Crouter, Eamonn Keogh
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 34, Nº 6, 2020, págs. 1713-1743
• Idioma: inglés
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• Resumen

  • The first question a data analyst asks when confronting a new dataset is often, “Show me some representative/typical data.” Answering this question is simple in many domains, with random samples or aggregate statistics of some kind. Surprisingly, it is difficult for large time series datasets. The major difficulty is not time or space complexity, but defining what it means to be representative data for this data type. In this work, we show that the obvious candidate definitions: motifs, shapelets, cluster centers, random samples etc., are all poor choices. We introduce time series snippets, a novel representation of typical time series subsequences. Informally, time series snippets can be seen as the answer to the following question. If a user, which could be a human or a higher-level algorithm, only has resources (including human time) to inspect k subsequences of a long time series, which k subsequences should be chosen? Beyond their utility for visualizing and summarizing massive time series collections, we show that time series snippets have utility for high-level comparison of large time series collections.