 Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)Ekin Can Erkuş and Vilda Purutçuoğlu European Journal of Operational Research, 2021, vol. 291, issue 2, 560-574 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. Keywords: Fourier transform; Periodicity; Optimization; Outlier detection (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:291:y:2021:i:2:p:560-574 DOI: 10.1016/j.ejor.2020.01.014 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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