Modeling Multiple Time Series for Anomaly Detection Philip Chan and Matthew Mahoney   View Paper (PDF)   Return to Session 1: Times Series I Abstract Our goal is to generate comprehensible and accurate models from multiple time series for anomaly detection. The models need to produce anomaly scores in an online manner for real-life monitoring tasks. We introduce three algorithms that work in a constructed feature space and evaluate them with a real data set from the NASA shuttle program. Our offline and online evaluations indicate that our algorithms can be more accurate than two existing algorithms. ©2006 Association for Computing Machinery