Download CaterpillarSSA 3.30
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 . The program works under Windows 9x/NT/2000/Me/XP. 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.
Features:
- Analysis of one-dimensional time series:
- Decomposition of one-dimensional time series into eigentriples (eigenvalues, eigenvectors and principal components)
- Convenient graphical visualization of results for identification of the eigentriples corresponding to trend, periodicities, noise
- Grouping of eigentriples that leads to expansion of the time series into additive components
- Reconstruction of time series components (trend, oscillations, periodicities, noise) by choice of eigentriples
- Residual analysis
- Forecast of one-dimensional time series:
- Approximation (local) of time series by finite-rank series
- Forecast by vector and recurrent methods
- Analyzing the linear recurrent formula used for the recurrent forecast method
- Confidence intervals by empirical and bootstrap methods
- Construction of envelopes (channels)
- Testing the forecast results on validation period
- Change-point detection for one-dimensional time series:
- Change-point detection by comparing the 'Caterpillar-SSA' structures of the base and test time series intervals
- Construction of heterogeneity matrix and detection functions
- Analyzing the found structural changes by moving root and modulus functions
- Multichannel Analysis/Forecast of time series:
- Simultaneous decomposition of several one-dimensional time series into common eigentriples (eigenvalues, eigenvectors and principal components)
- Convenient graphical visualization of results for identification of the eigentriples corresponding to trend, common periodicities, noise
- Grouping of eigentriples that leads to expansion of the time series into additive components
- Reconstruction of the time series components (trend, oscillations, periodicities, noise) by choice of eigentriples
- Approximation (local) of time series by finite-rank series
- Forecast by vector and recurrent methods
- Testing the forecast results on validation period
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CaterpillarSSA 3.30 (30 days trial version)