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 aforementioned properties these are difficult to obtain. Temporal Blind Source Separation (TBSS) is a suitable and well-established framework for these data. However, the large choice of methods and their tuning parameters impedes the effective use of TBSS in practice. The goal of Visual Analytics (VA) is 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. In this paper, we highlight the benefits and opportunities of VA concepts for statistic-oriented problems using a real-world TBSS application example with a dataset of climate and meteorological measurements in Italy.

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