Anomaly detection using sliding causal windows

Anomaly detection using sliding windows is not new but using sliding causal windows has not been explored in the past. The need of causality arises from real time processing where the used sliding windows should not include future data samples that have not been visited, i.e., data samples come in after the currently being processed data sample. This paper develops an approach to anomaly detection using sliding causal windows that has capability of being implemented in real time. In doing so two types of causal windows are defined, causal matrix window and causal array window from which a causal sample covariance/correlation matrix can be derived. As for the causal array window recursive update equations are also derived and thus, speed up real time processing.