Visual‐Interactive Preprocessing of Multivariate Time Series Data. (10th July 2019)

• Record Type: Journal Article
• Authors: Bernard, Jürgen; Hutter, Marco; Reinemuth, Heiko; Pfeifer, Hendrik; Bors, Christian; Kohlhammer, Jörn
• Is Part Of: Computer graphics forum. Volume 38:Number 3(2019)
• Page Start: 401
• Page End: 412
• Publication Date: 2019-07-10
• Subjects: CCS Concepts -- Mathematics of computing → Time series analysis -- Human‐centered computing → Visual analytics; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13698
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: Pre‐processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre‐processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre‐processing pipelines, human‐in‐the‐loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in‐depth research in visual analytics. We present a visual‐interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre‐processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty‐aware pre‐processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre‐processing in general and for uncertainty‐aware pre‐processing of multivariate time series in particular.