Journal article 917 views 187 downloads
TimeCluster: dimension reduction applied to temporal data for visual analytics
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Xianghua Xie ,
Mark Williams
The Visual Computer, Volume: 35, Issue: 6-8, Pages: 1013 - 1026
Swansea University Authors:
Mark Jones , Xianghua Xie
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DOI (Published version): 10.1007/s00371-019-01673-y
Abstract
With the increase of temporal data, there is a growing need for advanced solutions which assist users to understand such data, observe its changes over the time, find repeated patterns, detect outliers, and effectively label data instances in long time-series data. Although these tasks are quite dis...
| Published in: | The Visual Computer |
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| ISSN: | 0178-2789 1432-2315 |
| Published: |
2019
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa49701 |
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| Abstract: |
With the increase of temporal data, there is a growing need for advanced solutions which assist users to understand such data, observe its changes over the time, find repeated patterns, detect outliers, and effectively label data instances in long time-series data. 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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| College: |
Faculty of Science and Engineering |
| Issue: |
6-8 |
| Start Page: |
1013 |
| End Page: |
1026 |