Structural Periodic Measures for Time-Series Data |
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Authors: | Michail Vlachos Philip S Yu Vittorio Castelli Christopher Meek |
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Affiliation: | (1) IBM T.J. Watson Research Center, 19 Skyline Dr, Hawthorne, NY, USA;(2) Microsoft Research, One Microsoft Way, Redmond, WA, USA |
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Abstract: | This work motivates the need for more flexible structural similarity measures between time-series sequences, which are based
on the extraction of important periodic features. Specifically, we present non-parametric methods for accurate periodicity
detection and we introduce new periodic distance measures for time-series sequences. We combine these new measures with an
effective metric tree index structure for efficiently answering k-Nearest-Neighbor queries. The goal of these tools and techniques are to assist in detecting, monitoring and visualizing structural
periodic changes. It is our belief that these methods can be directly applicable in the manufacturing industry for preventive
maintenance and in the medical sciences for accurate classification and anomaly detection.
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Keywords: | periodicity estimation periodogram autocorrelation phase-invariant matching metric index |
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