We are glad to announce that the paper "Visual Interactive Parameter Selection for Temporal Blind Source Separation" by Claudia Capello, Nikolaus Piccolotto, Christoph Muehlmann, Markus Bögl, Peter Filzmoser, Silvia Miksch, and Klaus Nordhausen was accepted to the Journal of Data Science, Statistics, and Visualisation.
Abstract:
Many fields of science and industry collect and analyze multivariate time-varying measurements, e.g., healthcare, geophysics, or finance. Such data is often high-dimensional, correlated, and noisy. Experts are interested in latent components of the dataset, but due to the properties above, these are difficult to obtain. Temporal Blind Source Separation (TBSS) is a suitable and well-established framework for these data. However, the wide choice of methods and their tuning parameters impede the effective use of TBSS in practice. Visual Analytics (VA) aims to create powerful analytic tools by combining the strengths of humans and computers. We designed, developed, and evaluated VA contributions in previous work to support TBSS-related analysis tasks. This paper highlights the benefits and opportunities of VA concepts for statistic-oriented problems. We demonstrate how their analysis workflow can be supported using an important TBSS application example with a real-world dataset of meteorological measurements in Italy.