Time Series and Multidimensional Streaming Outlier Detection

Abstract

The temporal and streaming outlier-detection scenarios arise in the context of many applications such as sensor data, mechanical systems diagnosis, medical data, network intrusion data, newswire text posts, or financial posts. In such problem settings, the assumption of temporal continuity plays a critical role in identifying outliers. Temporal continuity refers to the fact that the patterns in the data are not expected to change abruptly, unless there are abnormal processes at work. It is worth noting that outlier analysis has diverse formulations in the context of temporal data, in some of which temporal continuity is more important than others. In time-series data, temporal continuity is immediate, and expected to be very strong. In multidimensional data with a temporal component (e.g., text streams), temporal continuity is much weaker, and is present only from the perspective of aggregate trends.