PEVis: visual analytics of potential anomaly pattern evolution for temporal multivariate data.

The availability of massive temporal multivariate data provides unprecedented opportunities to analyze anomaly pattern evolution. However, due to the complicated relationships and dynamic temporality, it is a challenging problem to efficiently reveal and perceive the evolution of abnormal patterns. Existing methods mostly focus on the use of automated algorithms to capture anomalies, which limits the understanding of the evolutionary law. In this paper, we propose a novel visual analytics system, PEVis, to help domain experts extract four types of abnormal evolution patterns and quantitatively analyze the contribution of different features to its occurrence. By aggregating different anomaly detection algorithms, the anomalous data that deviate seriously from other densely distributed points or with small isolated clusters can be identified. A strategy for perceiving temporal multivariate data is proposed to solve the conflict between exploring the global time-varying law and local data distribution in visualization, through aligning dimension reduction space of adjacent timestamp and designing a doughnut glyph to visually display data distribution and feature contributions. Experiments on two real-world datasets, namely air quality index data and consumer price index data, demonstrate that the proposed methods can effectively reveal the seasonal variation of abnormal phenomena. [ABSTRACT FROM AUTHOR]