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

摘要

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 berepresentativedata 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 introducetime 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 inspectksubsequences of a long time series, whichksubsequences 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.