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On Periodicity Detection and Structural Periodic Similarity

2005-04-21Proceedings of the 2005 SIAM International Conference on Data Mining 2005Code Available0· sign in to hype

Michail Vlachos, Philip Yu, Vittorio Castelli

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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. 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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