Vernier, Eduardo FaccinGarcia, RafaelSilva, Iron Prando daComba, João L. D.Telea, Alexandru C.Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana2020-05-242020-05-2420201467-8659https://doi.org/10.1111/cgf.13977https://diglib.eg.org:443/handle/10.1111/cgf13977Dimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time-dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time-dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 9 methods, 10 datasets, and 12 quality metrics, and elect the best-suited methods for projecting time-dependent multivariate data, exploring the design choices and characteristics of each method. Additional results can be found in the online benchmark repository. We designed our evaluation pipeline and benchmark specifically to be a live resource, open to all researchers who can further add their favorite datasets and techniques at any point in the future.Attribution 4.0 International LicenseComputing methodologiesDimensionality reduction and manifold learning10.1111/cgf.13977241-252