Modeling multiple time series for anomaly detection

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.