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Interactive Visualization of Multivariate Time Series Data

  • Shawn MartinEmail author
  • Tu-Toan Quach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9744)

Abstract

Organizing multivariate time series data for presentation to an analyst is a challenging task. Typically, a dataset contains hundreds or thousands of datapoints, and each datapoint consists of dozens of time series measurements. Analysts are interested in how the datapoints are related, which measurements drive trends and/or produce clusters, and how the clusters are related to available metadata. In addition, interest in particular time series measurements will change depending on what the analyst is trying to understand about the dataset.

Rather than providing a monolithic single use machine learning solution, we have developed a system that encourages analyst interaction. This system, Dial-A-Cluster (DAC), uses multidimensional scaling to provide a visualization of the datapoints depending on distance measures provided for each time series. The analyst can interactively adjust (dial) the relative influence of each time series to change the visualization (and resulting clusters). Additional computations are provided which optimize the visualization according to metadata of interest and rank time series measurements according to their influence on analyst selected clusters.

The DAC system is a plug-in for Slycat (slycat.readthedocs.org), a framework which provides a web server, database, and Python infrastructure. The DAC web application allows an analyst to keep track of multiple datasets and interact with each as described above. It requires no installation, runs on any platform, and enables analyst collaboration. We anticipate an open source release in the near future.

Keywords

Multivariate time series Multidimensional scaling Interactive visualization Slycat 

Notes

Acknowledgements

Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. This work was funded by Sandia Laboratory Directed Research and Development (LDRD).

References

  1. 1.
    Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Springer, London (2011)CrossRefGoogle Scholar
  2. 2.
    Aggarwal, C.C.: Outlier Analysis. Springer, New York (2013)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bakshi, B., Stephanopoulos, G.: Representation of process Trends-IV. Induction of real-time patterns from operating data for diagnosis and supervisory control. Comput. Chem. Eng. 18(4), 303–332 (1994)CrossRefGoogle Scholar
  4. 4.
    Berkhin, P.: A survey of clustering data mining techniques. In: Kogan, J., Nicholas, C., Teboulle, M. (eds.) Grouping Multidimensional Data, pp. 25–71. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Bertini, E., Lalanne, D.: Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. In: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery (VAKD 2009), pp. 12–20 (2009)Google Scholar
  6. 6.
    Borg, I., Goenen, P.J.F.: Modern Multidimensional Scaling. Springer, New York (2005)Google Scholar
  7. 7.
    Crossno, P.J., Shead, T.M., Sielicki, M.A., Hunt, W.L., Martin, S., Hsieh, M.-Y.: Slycat ensemble analysis of electrical circuit simulations. In: Bennett, J., Vivodtzev, F., Pascucci, V. (eds.) Topological and Statistical Methods for Complex Data. Mathematics and Visualization, pp. 279–294. Springer, Heidelberg (2015)Google Scholar
  8. 8.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons, Hoboken (2000)zbMATHGoogle Scholar
  9. 9.
    Esling, P., Agon, C.: Time-Series data mining. ACM Comp. Surv. 45(1), 12 (2012)CrossRefzbMATHGoogle Scholar
  10. 10.
    Faloutsos, C., Ranganathan, M., Manolopulos, Y.: Fast subsequence matching in time-series databases. SIGMOD Rec. 23, 419–429 (1994)CrossRefGoogle Scholar
  11. 11.
    Fontes, C.H., Pereira, O.: Pattern recognition in multivariate time series - a case study applied to fault detection in a gas turbine. Eng. Appl. Artif. Intell. 49, 10–18 (2016)CrossRefGoogle Scholar
  12. 12.
    Inselberg, A.: A survey of parallel coordinates. In: Hege, H.-C., Polthier, K. (eds.) Mathematical Visualization, pp. 167–179. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  13. 13.
    Kabsch, W.: A solution for the best rotation to relate two sets of vectors. Acta Crystallogr. Sect. A 32(5), 922–923 (1976)CrossRefGoogle Scholar
  14. 14.
    Keim, D.A., Kohlhammer, J., Ellis, G., Mansmann, F.: Mastering the information age - solving problems with visual analytics. Eurographics (2010)Google Scholar
  15. 15.
    Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Min. Knowl. Discov. 7(4), 349–371 (2003)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Keogh, E., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: a survey and novel approach. Data Min. Time Ser. Databases 57, 1–22 (2004)CrossRefGoogle Scholar
  17. 17.
    Keogh, E., Lonardi, S., Ratanamahatana, C.: Towards parameter-free data mining. In: Proceedings of the 10th ACM International Conference on Knowledge Discovery and Data Mining, pp. 206–215 (2004)Google Scholar
  18. 18.
    Konyha, Z., Lez, A., Matkovic, K., Jelovic, M., Hauser, H.: Interactive visualization analysis of families of curves using data aggregation and derivation. In: Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies, pp. 1–24 (2012)Google Scholar
  19. 19.
    Lawson, C.L., Hanson, R.J.: Solving Least Squares Problems, vol. 161. Prentice-Hall, Upper Saddle River (1974)zbMATHGoogle Scholar
  20. 20.
    Lin, J., Keogh, E.: Clustering of time-series subsequences is meaningless: implications for previous and future research. Knowl. Inf. Syst. 8(2), 154–177 (2005)CrossRefGoogle Scholar
  21. 21.
    Lin, T., Kaminski, N., Bar-Joseph, Z.: Alignment and classification of time series gene expression in clinical studies. Bioinf. 24(13), 147–155 (2008)CrossRefGoogle Scholar
  22. 22.
    Muller, W., Schumann, H.: Visualization methods for time-dependent data - an overview. In: Proceedings of the Winter Simulation Conference, pp. 737–745 (2003)Google Scholar
  23. 23.
    Ouyang, R., Ren, L., Cheng, W., Zhou, C.: Similarity search and pattern discovery in hydrological time series data mining. Hydrol. Process. 24(9), 1198–1210 (2010)CrossRefGoogle Scholar
  24. 24.
    Patel, P., Keogh, E., Lin, J., Lonardi, S.: Mining motifs in massive time series databases. In: Proceedings of the IEEE International Conference on Data Mining (ICDM 2002), pp. 370–377 (2002)Google Scholar
  25. 25.
    Peng, R.: A method for visualizing multivariate time series data. J. Stat. Soft. 25(1), 1–17 (2008)Google Scholar
  26. 26.
    Silva, S.F., Catarci, T.: Visualization of linear time-oriented data: a survey. In: Proceedings of the International Conference on Web Information Systems Engineering, p. 310 (2000)Google Scholar
  27. 27.
    Singhal, A., Seborg, D.E.: Clustering multivariate time-series data. J. Chemometr. 19(8), 427–438 (2005)CrossRefGoogle Scholar
  28. 28.
    Song, H., Li, G.: Tourism demand modelling and forecasting - a review of recent research. Tour. Manag. 29(2), 203–220 (2008)CrossRefGoogle Scholar
  29. 29.
    Thakur, S., Rhyne, T.-M.: Data vases: 2D and 3D plots for visualizing multiple time series. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009, Part II. LNCS, vol. 5876, pp. 929–938. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  30. 30.
    Thomas, J. J., Cook, K. A.: Illuminating the path: the research and developmentagenda for visual analytics. Nat. Vis. & Anal. Ctr. (2005)Google Scholar
  31. 31.
    Zhong, S., Khoshgoftaar, T., Seliya, N.: Clustering-Based network intrusion detection. Int. J. Reliab. Qual. Saf. Eng. 14(2), 169–187 (2007)CrossRefGoogle Scholar
  32. 32.
    Zhu, J., Wang, B., Wu, B.: Social network users clustering based on multivariate time series of emotional behavior. J. China Univ. Posts Telecom. 21(2), 21–31 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Sandia National LaboratoriesAlbuquerqueUSA

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