CaterpillarSSA

The 32-bit CaterpillarSSA program performs extended analysis, forecasting and change-point detection for one-dimensional time series and analysis/forecast of multi-dimensional time series. Macros tools, which serve to remember sequences of program procedures and to perform them automatically, are added (see macros description macros.rtf 470kb or macros.zip 44kb). The program works under Windows 9x/NT/2000/Me/XP/Vista/W7. You can download evaluation version and try it for 30 days. All the examples of the book ”Analysis of time series structure: SSA and related techniques” are obtained by means of the program. Therefore, this book can be considered as an additional help to the program.


References in zbMATH (referenced in 46 articles )

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  1. Kume, Kenji; Nose-Togawa, Naoko: An adaptive orthogonal SSA decomposition algorithm for a time series (2018)
  2. Noonan, Jack; Zhigljavsky, Anatoly: Approximations of the boundary crossing probabilities for the maximum of moving weighted sums (2018)
  3. Rodrigues, Paulo Canas; Mahmoudvand, Rahim: The benefits of multivariate singular spectrum analysis over the univariate version (2018)
  4. Kouchaki, Samaneh; Sanei, Saeid: Tensor factorisation for narrowband single channel source decomposition (2017)
  5. Launonen, Ilkka; Holmström, Lasse: Multivariate posterior singular spectrum analysis (2017)
  6. Viljoen, Helena: A comparison of stepwise common singular spectrum analysis and horizontal multi-channel singular spectrum analysis (2017)
  7. Alharbi, Nader; Hassani, Hossein: A new approach for selecting the number of the eigenvalues in singular spectrum analysis (2016)
  8. Golyandina, N. E.; Lomtev, M. A.: Improvement of separability of time series in singular spectrum analysis using the method of independent component analysis (2016)
  9. Hojjati Tavassoli, Zahra; Iranmanesh, Seyed Hossein; Tavassoli Hojjati, Ahmad: Designing a framework to improve time series data of construction projects: application of a simulation model and singular spectrum analysis (2016)
  10. Huang, Xu; Ghodsi, Mansi; Hassani, Hossein: A novel similarity measure based on eigenvalue distribution (2016)
  11. Pulkkinen, Seppo: Nonlinear kernel density principal component analysis with application to climate data (2016)
  12. Sergeyev, Yaroslav D.; Kvasov, Dmitri E.; Mukhametzhanov, Marat S.: On the least-squares fitting of data by sinusoids (2016)
  13. Thomakos, Dimitrios: Smoothing non-stationary time series using the discrete cosine transform (2016)
  14. Yarmohammadi, Masoud; Kalantari, Mahdi; Mahmoudvand, Rahim: Empirical comparison of Box-Jenkins models, artificial neural network and singular spectrum analysis in forecasting time series (2016)
  15. Gillard, J. W.; Zhigljavsky, A. A.: Stochastic algorithms for solving structured low-rank matrix approximation problems (2015)
  16. Ivanov, V. V.; Klimanov, S. G.; Kryanev, A. V.; Lukin, G. V.; Udumyan, D. K.: Prediction of chaotic dynamical processes based on detection of regular component (2015)
  17. Jain, Pooja; Pachori, Ram Bilas: An iterative approach for decomposition of multi-component non-stationary signals based on eigenvalue decomposition of the Hankel matrix (2015)
  18. Shcherbinin, V. V.: Comparison of families of algorithms for recognizing abnormal behavior of dynamic systems (2015)
  19. Wang, Rui; Ma, Hong-Guang; Liu, Guo-Qing; Zuo, Dong-Guang: selection of window length for singular spectrum analysis (2015)
  20. Chu, Moody T.; Lin, Matthew M.; Wang, Liqi: A study of singular spectrum analysis with global optimization techniques (2014)

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