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Outlier detection methods for time series datasets
Date
2018-04-28
Author
Erkuş, Ekin Can
Purutçuoğlu Gazi, Vilda
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https://hdl.handle.net/11511/73899
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Kalman filter (KF), which is an algorithm that is utilized to estimate unknown variables based on noisy measurements, has been successfully employed in many applications such as navigation, control, signal processing and target tracking. It is the optimum Bayesian filter in terms of mean square error (MSE) for linear Gaussian state-space models (SSMs). However, in many real world applications, the performance of KF degrades due to the presence of outliers in noises. Motivated by this problem, several algori...
Outlier Detection and Quasi-periodicity Optimization Algorithm: Frequency Domain Based Outlier Detection (FOD)
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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...
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E. C. Erkuş and V. Purutçuoğlu Gazi, “Outlier detection methods for time series datasets,” 2018, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/73899.