An Outlier Analysis on Multi-Dimensional and Time-Series Data

Outlier detection refers to the detection of unexpected situations in the data. Outliers are fraud, hacking, mislabeled data, or unusual behavior in the system. Therefore, it is important to determine these values. In this study, outlier detection performances of the algorithms used in outlier detection analysis on different types of data sets were calculated and compared. As a result of the study, it was seen that the algorithms showed sufficient success. The highest performance was seen in the Histogram-based outlier detection algorithm with 99 % accuracy