Finding Surprising Patterns in a Time Series Database in Linear Time and Space

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Finding Surprising Patterns in a Time Series Database in Linear Time and Space (2002)

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by Eamonn Keogh , Stefano Lonardi , Bill 'Yuan-chi' Chiu

Venue:	In In proc. of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Citations:	138 - 8 self

BibTeX

@INPROCEEDINGS{Keogh02findingsurprising,
    author = {Eamonn Keogh and Stefano Lonardi and Bill 'Yuan-chi' Chiu},
    title = {Finding Surprising Patterns in a Time Series Database in Linear Time and Space},
    booktitle = {In In proc. of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
    year = {2002},
    pages = {550--556},
    publisher = {ACM Press}
}

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Abstract

The problem of finding a specified pattern in a time series database (i.e. query by content) has received much attention and is now a relatively mature field. In contrast, the important problem of enumerating all surprising or interesting patterns has received far less attention. This problem requires a meaningful definition of "surprise", and an efficient search technique. All previous attempts at finding surprising patterns in time series use a very limited notion of surprise, and/or do not scale to massive datasets. To overcome these lim- itations we introduce a novel technique that defines a pattern surprising if the frequency of its occurrence differs substantially from that expected by chance, given some previously seen data. This notion has the advantage of not requiring an explicit definition of surprise, which may be impossible to elicit from a domain expert. Instead the user simply gives the algorithm a collection of previously observed normal data. Our algorithm uses a suffix tree to efficiently encode the frequency of all observed patterns and allows a Markov model to predict the expected frequency of previously unobserved patterns. Once the suffix tree has been constructed, a measure of surprise for all the patterns in a new database can be determined in time and space linear in the size of the database. We demonstrate the utility of our approach with an extensive experimental evaluation.

Keyphrases

surprising pattern time series database linear time suffix tree efficient search technique mature field novel technique previous attempt specified pattern normal data pattern surprising meaningful definition explicit definition occurrence differs massive datasets important problem limited notion space linear much attention interesting pattern new database domain expert extensive experimental evaluation lim itations time series expected frequency markov model

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