Open AccessComparison of outlier detection techniques in non-stationary time series data
Author(s) -
Sampson Twumasi-Ankrah,
Simon Kojo Appiah,
Doris Arthur,
Wilhemina Adoma Pels,
Jonathan Kwaku Afriyie,
Danielson Nartey
Publication year - 2021
Publication title -
global journal of pure and applied sciences.
Language(s) - English
Resource type - Journals
ISSN - 1118-0579
DOI - 10.4314/gjpas.v27i1.7
Subject(s) - mahalanobis distance , outlier , anomaly detection , pattern recognition (psychology) , robust principal component analysis , computer science , principal component analysis , maxima and minima , artificial intelligence , extreme value theory , local outlier factor , mathematics , data mining , statistics , mathematical analysis
This study examined the performance of six outlier detection techniques using a non-stationary time series dataset. Two key issues were of interest. Scenario one was the method that could correctly detect the number of outliers introduced into the dataset whiles scenario two was to find the technique that would over detect the number of outliers introduced into the dataset, when a dataset contains only extreme maxima values, extreme minima values or both. Air passenger dataset was used with different outliers or extreme values ranging from 1 to 10 and 40. The six outlier detection techniques used in this study were Mahalanobis distance, depth-based, robust kernel-based outlier factor (RKOF), generalized dispersion, Kth nearest neighbors distance (KNND), and principal component (PC) methods. When detecting extreme maxima, the Mahalanobis and the principal component methods performed better in correctly detecting outliers in the dataset. Also, the Mahalanobis method could identify more outliers than the others, making it the "best" method for the extreme minima category. The kth nearest neighbor distance method was the "best" method for not over-detecting the number of outliers for extreme minima. However, the Mahalanobis distance and the principal component methods were the "best" performed methods for not over-detecting the number of outliers for the extreme maxima category. Therefore, the Mahalanobis outlier detection technique is recommended for detecting outlier in nonstationary time series data.