Online novelty detection on temporal sequences

: Novelty detection, or anomaly detection, on temporal sequences has increasingly attracted attention from researchers in different areas. In this paper, we present a new framework for online novelty detection on temporal sequences. This framework includes a mechanism for associating each detection result with a confidence value. Based on this framework, we develop a concrete online detection algorithm, by modeling the temporal sequence using an online support vector regression algorithm. Experiments on both synthetic and real world data are performed to demonstrate the promising performance of our proposed detection algorithm.

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Junshui Ma, Simon Perkins
• Keywords: Data Mining, KDD 2003, Online Detection Algorithm, Temporal Sequences, Vector Regression Algorithm