Structural Periodic Measures for Time-Series Data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2006-01

AUTHORS

Michail Vlachos, Philip S. Yu, Vittorio Castelli, Christopher Meek

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. More... »

PAGES

1-28

References to SciGraph publications

  • 2004. Using Convolution to Mine Obscure Periodic Patterns in One Pass in ADVANCES IN DATABASE TECHNOLOGY - EDBT 2004
  • 2000-07. Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances in THE VLDB JOURNAL
  • 1993. Efficient similarity search in sequence databases in FOUNDATIONS OF DATA ORGANIZATION AND ALGORITHMS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10618-005-0016-4

    DOI

    http://dx.doi.org/10.1007/s10618-005-0016-4

    DIMENSIONS

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