Outlier Detection and Quasi-periodicity Optimization Algorithm: Frequency Domain Based Outlier Detection (FOD)

2020-01-01
Erkuş, Ekin Can
Purutçuoğlu Gazi, Vilda
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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 meth- ods 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.
Fourier transform, Periodicity, Optimization, Outlier detection
https://hdl.handle.net/11511/80033
https://www.sciencedirect.com/science/article/pii/S0377221720300357
European Journal Of Operational Research
Graduate School of Natural and Applied Sciences, Article

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Citation Formats
E. C. Erkuş and V. Purutçuoğlu Gazi, “Outlier Detection and Quasi-periodicity Optimization Algorithm: Frequency Domain Based Outlier Detection (FOD),” European Journal Of Operational Research, pp. 1–15, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/80033.
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