article

TimeCluster: dimension reduction applied to temporal data for visual analytics

Authors:

Mark WilliamsAuthors Info & Claims

The Visual Computer: International Journal of Computer Graphics, Volume 35, Issue 6-8

Pages 1013 - 1026

https://doi.org/10.1007/s00371-019-01673-y

Published: 01 June 2019 Publication History

Abstract

There is a need for solutions which assist users to understand long time-series data by observing its changes over time, finding repeated patterns, detecting outliers, and effectively labeling data instances. Although these tasks are quite distinct and are usually tackled separately, we present an interactive visual analytics system and approach that can address these issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series datasets and report two real-world case studies that are used to evaluate our system.

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TimeCluster: dimension reduction applied to temporal data for visual analytics

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cover image The Visual Computer: International Journal of Computer Graphics

The Visual Computer: International Journal of Computer Graphics Volume 35, Issue 6-8

June 2019

415 pages

ISSN:0178-2789

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Copyright © Copyright © 2019 Springer-Verlag GmbH Germany, part of Springer Nature.

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Berlin, Heidelberg

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Published: 01 June 2019

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