MANDALA—Visual Exploration of Anomalies in Industrial Multivariate Time Series Data

The detection, description and understanding of anomalies in multivariate time series data is an important task in several industrial domains. Automated data analysis provides many tools and algorithms to detect anomalies, while visual interfaces enable domain experts to explore and analyze data interactively to gain insights using their expertise. Anomalies in multivariate time series can be diverse with respect to the dimensions, temporal occurrence and length within a dataset. Their detection and description depend on the analyst's domain, task and background knowledge. Therefore, anomaly analysis is often an underspecified problem. We propose a visual analytics tool called MANDALA (Multivariate ANomaly Detection And expLorAtion), which uses kernel density estimation to detect anomalies and provides users with visual means to explore and explain them. To assess our algorithm's effectiveness, we evaluate its ability to identify different types of anomalies using a synthetic dataset generated with the GutenTAG anomaly and time series generator. Our approach allows users to define normal data interactively first. Next, they can explore anomaly candidates, their related dimensions and their temporal scope. Our carefully designed visual analytics components include a tailored scatterplot matrix with semantic zooming features that visualize normal data through hexagonal binning plots and overlay candidate anomaly data as scatterplots. In addition, the system supports the analysis on a broader scope involving all dimensions simultaneously or on a smaller scope involving dimension pairs only. We define a taxonomy of important types of anomaly patterns, which can guide the interactive analysis process. The effectiveness of our system is demonstrated through a use case scenario on industrial data conducted with domain experts from the automotive domain and a user study utilizing a public dataset from the aviation domain.