HOT SAX: Finding the Most Unusual Time Series Subsequence: Algorithms and Applications
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Finding the Unusual Medical Time Series: Algorithms and Applications
In this work we introduce the new problem of finding time series discords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While discords have many uses for data mining, they are particularly attractive as anomaly detectors b...
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Finding discords in time series database is an important problem in the last decade due to its variety of real-world applications, including data cleansing, fault diagnostics, and financial data analysis. The best known approach to our knowledge is HOT SAX technique based on the equiprobable distribution of SAX representations of time series. This characteristic, however, is not preserved in th...
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تاریخ انتشار 2005