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Mathematical Problems in Engineering
Volume 2011 (2011), Article ID 793429, 14 pages
Research Article

Symplectic Principal Component Analysis: A New Method for Time Series Analysis

Institute of Vibration, Shock & Noise and State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200030, China

Received 9 July 2011; Accepted 22 September 2011

Academic Editor: Mahmoud T. Yassen

Copyright © 2011 Min Lei and Guang Meng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [5 citations]

The following is the list of published articles that have cited the current article.

  • Hong-Bo Xie, and Socrates Dokos, “A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 23, no. 2, pp. 023131, 2013. View at Publisher · View at Google Scholar
  • Hong-Bo Xie, and Socrates Dokos, “A symplectic geometry-based method for nonlinear time series decomposition and prediction,” Applied Physics Letters, vol. 103, no. 5, pp. 054103, 2013. View at Publisher · View at Google Scholar
  • Hong-Bo Xie, Tianruo Guo, Bellie Sivakumar, Alan Wee-Chung Liew, and Socrates Dokos, “Symplectic geometry spectrum analysis of nonlinear time series,” Proceedings of The Royal Society A-Mathematical Physical and Engineering Sc, vol. 470, no. 2170, 2014. View at Publisher · View at Google Scholar
  • Giuseppe Orlando, “A discrete mathematical model for chaotic dynamics in economics: Kaldor's model on business cycle,” Mathematics And Computers In Simulation, vol. 125, pp. 83–98, 2016. View at Publisher · View at Google Scholar
  • Min Lei, Guang Meng, Wenming Zhang, Joshua Wade, and Nilanjan Sarkar, “Symplectic Entropy as a Novel Measure for Complex Systems,” Entropy, vol. 18, no. 11, pp. 412, 2016. View at Publisher · View at Google Scholar