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Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)

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  • Erkuş, Ekin Can
  • Purutçuoğlu, Vilda

Abstract

Outlier detection is one of the main challenges in the pre-processing stage of data analyses. In this study, we suggest a new non-parametric outlier detection technique which is based on the frequency-domain and Fourier Transform definitions and call it as the frequency-domain based outlier detection (FOD). From simulation results under various distributions and real data applications, we observe that our proposal approach is capable of detecting quasi-periodic outliers in time series data more successfully compared with other commonly used methods like z-score, box-plot and also faster than some specialized methods Grubbs method and autonomous anomaly detection (AAD) method. Therefore, we consider that our proposal approach can be an alternative approach to find quasi-periodic outliers in time series data.

Suggested Citation

  • Erkuş, Ekin Can & Purutçuoğlu, Vilda, 2021. "Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)," European Journal of Operational Research, Elsevier, vol. 291(2), pages 560-574.
    • Handle: RePEc:eee:ejores:v:291:y:2021:i:2:p:560-574
    DOI: 10.1016/j.ejor.2020.01.014
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