Anomaly detection in streaming nonstationary temporal data

This article proposes a framework that provides early detection of anomalous series within a large collection of non-stationary streaming time series data. We define an anomaly as an observation that is very unlikely given the recent distribution of a given system. The proposed framework first forecasts a boundary for the system’s typical behavior using extreme value theory. Then a sliding window is used to test for anomalous series within a newly arrived collection of series.<img src=“http://feeds.feedburner.com/~r/ProfessorRobJHyndman/~4/8gmXqqqhYwM" height=“1” width=“1” …