A Synthesis Plot of PCP and MDS for the Exploration of High Dimensional Time Series Data

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• 关键词：Multivariate data ; Temporal data ; Parallel Coordinates ; MDS ; Visualization
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10092
• 期：1
• 页码：38-45
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• 作者单位：Hao Ma (17)
  Yingmei Wei (17)
  Xiaolei Du (17)
  
  17. National University of Defense Technology, Changsha, China
  
• 丛书名：Transactions on Edutainment XIII
• ISBN：978-3-662-54395-5
• 卷排序：10092

文摘

Nowadays, high dimensional time series data draws more and more attention. But it is a great challenge to analyze high dimensional time series data. At present, typical methods for high dimensional time series data visualization, including ThemeRiver and Parallel Coordinates Plots, cannot reveal the distribution of the data state nor the evolution of data with time variation. And they also cannot explore the relationship between attributes of the high dimensional data and data state. In this paper, a synthetic visualization system combining Parallel Coordinates Plots and Multidimensional Scaling (MDS) is proposed for the analysis of multivariate time series data. The state distribution diagram is firstly achieved by mapping high dimensional series data onto the two-dimension space using MDS method. Distance of data points on the state distribution diagram reflects the similarity within time slices while the density indicates the state distribution of the dataset. The original dataset is then mapped on the Parallel Coordinates. Through the interaction of Parallel Coordinates and the state distribution diagram, users are able to detect evolution of time series data and explore the relationship within multiple dimensions under different states of data.