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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Due to the assumption of the Fourier Transform that the data is periodic, proper windowing of the data might be necessary for achieving a more accurate harmonic analysis. In order to enhance the flow of the paper, we will not go into describing windowing techniques, but direct the interested reader to Harris (1978) for an excellent review.
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Acknowledgments
We are thankful to MSN and Microsoft for letting us use a portion of the MSN query logs. We would like also to thank Zografoula Vagena for her help in the experimental section. Finally, we acknowledge the useful comments of the anonymous reviewers, that have helped us present this work in a more constructive and complete way.
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Vlachos, M., Yu, P.S., Castelli, V. et al. Structural Periodic Measures for Time-Series Data. Data Min Knowl Disc 12, 1–28 (2006). https://doi.org/10.1007/s10618-005-0016-4
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DOI: https://doi.org/10.1007/s10618-005-0016-4