Anomaly Detection Using Unsupervised Profiling Method in Time Series Data.
Zakia Ferdousi, Akira Maeda · 2006
Abstract. The anomaly detection problem has important applications in the field of fraud detection, network robustness analysis and intrusion detection. This paper is concerned with the problem of detecting anomalies in time series data using Peer Group Analysis (PGA), which is an unsupervised technique. The objective of PGA is to characterize the expected pattern of behavior around the target sequence in terms of the behavior of similar objects and then to detect any differences in evolution between the expected pattern and the target. The experimental results demonstrate that the method is able to flag anomalous re-cords effectively.