Assumption-Free Anomaly Detection in Time Series

Recent advancements in sensor technology have made it possible to collect enormous amounts of data in real time. However, because of the sheer volume of data most of it will never be inspected by an algorithm, much less a human being. One way to mitigate this problem is to perform some type of anomaly (novelty / interestingness/ surprisingness) detection and flag unusual patterns for further inspection by humans or more CPU intensive algorithms. Most current solutions are “custom made” for particular domains, such as ECG monitoring, valve pressure monitoring, etc. This customization requires extensive effort by domain expert. Furthermore, handcrafted systems tend to be very brittle to concept drift. In this demonstration, we will show an online anomaly detection system that does not need to be customized for individual domains, yet performs with exceptionally high precision/recall. The system is based on the recently introduced idea of time series bitmaps. To demonstrate the unive...

• Type: Conference
• Year: 2005
• Where: SSDBM
• Authors: Li Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana
• Keywords: CPU Intensive Algorithms, Database, Flag Unusual Patterns, Online Anomaly Detection, SSDBM 2005