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STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases

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Abstract

Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise.

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Authors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, Alberta, Canada

    Faraz Rasheed & Reda Alhajj

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  1. Faraz Rasheed
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  2. Reda Alhajj
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Correspondence to Reda Alhajj.

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Rasheed, F., Alhajj, R. STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases. Appl Intell 32, 267–278 (2010). https://doi.org/10.1007/s10489-008-0144-9

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  • DOI: https://doi.org/10.1007/s10489-008-0144-9

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