TimeCluster: dimension reduction applied to temporal data for visual analytics
TimeCluster: dimension reduction applied to temporal data for visual analytics
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DOI：
10.1007/s00371-019-01673-y
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发表时间：
2019-06-01
期刊：
影响因子：
3.5
通讯作者：
Williams, Mark
中科院分区：
文献类型：
--
作者：
Ali, Mohammed;Jones, Mark W.;Williams, Mark
需要一些解决方案，通过观察长时间序列数据随时间的变化、发现重复的模式、检测异常值和有效地标记数据实例，帮助用户理解长时间序列数据。虽然这些任务是截然不同的，通常是分开处理的，但我们提出了一个交互式可视化分析系统和方法，可以在单个系统中解决这些问题。它使用户能够使用连接的散点图在一张图像中可视化，理解和探索单变量或多变量长时间序列数据。它支持模式发现和异常值检测的交互式分析和探索。在我们的系统中使用了不同的降维技术并进行了比较。由于其提取特征的能力，深度学习与二维约简技术一起用于多变量时间序列，以便快速，轻松地解释和与大量时间序列数据交互。我们使用不同的时间序列数据集部署我们的系统，并报告了两个用于评估我们系统的真实案例研究。
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.