NetScatter: Visual analytics of multivariate time series with a hybrid of dynamic and static variable relationships

The ability to capture common characteristics among complex multi-variate time series variables can profoundly impact big data analytics in uncovering valuable insights into the relationships among them and enabling a dimensionality reduction technique. Two widely used data displays include time series and scatter plots. While the former focuses on the dynamics over time, the latter deals with static relationships among variables. Motivated by these distinctive perspectives, our research aims to maximally utilize the information captured by both at the same time. This paper presents NetScatter, a visual analytic approach to characterizing changes of pairwise relationships in a high-dimensional time series. Unlike most traditional techniques that employ a single perspective of the visual display, our approach combines static perspectives of two variables in multi-variate time series into a single representation by comparing all data instances over two different time steps. The paper also introduces a list of visual features of the representation to capture how overall data evolve. We have implemented a web-based prototype that supports a full range of operations, such as ranking, filtering, and details on demand. The paper illustrates the proposed approach on data of various sizes in different domains to demonstrate its benefits.