Detection of hidden patterns in time series data via multiple-time FOD method

The periodicity in time series data can be detected by several frequency domains’ methods, especially, by the Fourier transform (FT). FT is a non-parametric method to convert the time domain data into the frequency domain and it is used in many engineering and data science applications. Recently this method which has been used to detect outliers in time series observations where the data may also include some systematic patterns is called “outlier detection via Fourier transform” (FOD). From our previous analyses via real and simulated time-course datasets, it has been shown that FOD is a promising technique to find periodic and non-periodic outliers in the data, particularly, when the sample size increases and the data are far from normal distribution. On the other hand, it has been observed that the multiple application of FOD is successful in order to detect the hidden patterns in real electrocardiogram (ECG) datasets since the pattern of FOD signals indicates differences between control and various types of heart diseases. Therefore, we consider that they can be applied for the pre-diagnosis of certain heart illnesses. In this study, we aim to extend the multiple time FOD by evaluating its performance comprehensively under distinct Monte Carlo scenarios such as different sample sizes, distributions and percentage of outliers. We consider that these analyses can be helpful to detect outliers and hidden patterns in distinct time-series data including ECG datasets.
Citation Formats
E. C. Erkuş and V. Purutçuoğlu Gazi, “Detection of hidden patterns in time series data via multiple-time FOD method,” presented at the 30th European Conference on Operational Research (EURO 2019) (23 - 26 Haziran 2019), Dublin, İrlanda, 2019, Accessed: 00, 2021. [Online]. Available: