Finding interesting patterns in numerical time series is a challenging task. It is often difficult to specify in advance what constitutes an ‘interesting’ pattern and patterns can occur at very different time scales. Our method enables the visual detection and exploration of interesting patterns across a wide range of time scales.
Publication: Sips, M., Köthur, P., Unger, A., Hege, H.-C., Dransch, D. (2012): A Visual Analytics Approach to Multiscale Exploration of Environmental Time Series. - IEEE Transactions on Visualization and Computer Graphics, 18, 12, p. 2899-2907.
In coorperation with Potsdam Institute for Climate Impact Research - Research Domain 4: Transdisciplinary Concepts & Methods, Zuse Institute Berlin: Visual Data Analysis