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

  • Hao Ma
  • Yingmei WeiEmail author
  • Xiaolei Du
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10092)


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.


Multivariate data Temporal data Parallel Coordinates MDS Visualization 



The authors wish to thank the anonymous reviews for their valuable comments. This work is supported by the National Natural Science Foundation of China (NSFC) under grant No. 61402487.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.National University of Defense TechnologyChangshaChina

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