Many simulations and experiments produce large amounts of spatiotemporal data, for example consisting of sets of two-dimensional positional recordings over a long time interval. The high dimensionality of the data, together with its complex time-dependent behaviors, greatly limits the possibilities of manual analysis using traditional tooling. This thesis presents a novel approach to the visual analysis of spatiotemporal ensemble data by combining an immersive and intuitive virtual reality (VR) interface with interactive machine learning elements. By defining queries for specific spatiotemporal patterns, users are able to arrange the entire ensemble in a three-dimensional workspace based on the similarity between members, while individual members and their temporal behavior can be examined in detail using an intuitive three-dimensional visualization utilizing space-time cubes. Through a small-scale user study, the workflow and VR implementation have been tested on their usability, together with a comparison between different interaction techniques in terms of task efficiency and user experience. Results show that even users with little VR experience responded positively to the three-dimensional interactions and intuitive data exploration, while also achieving high ratings in immersion and engagement, despite an initial learning curve and some visual clarity issues.