| Company | GistaT Group |
| Website | http://www.gistatgroup.com/ |
| Country | Russia |
| Email | info@gistatgroup.com |
| Os | Win95, Win98, WinME, WinNT 3.x, WinNT 4.x, Windows2000 |
| Requirements | 16MB RAM |
| Language | English, Russian |
| Release Date | 1 07 2004 |
| License | Shareware |
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PC:Windows:Education:Mathematics
Views 1590 (+1) / Rating 5.25 / Shareware By GistaT Group
The programme is based on the right model-free method of time series analysis Caterpillar (another nominate is SSA - Singular Spectrum Analysis). It combines advantages of other methods with simplicity of visual control aids. The basic Caterpillar-SSA algorithm for analyzing one-dimensional time series consists of transformation of the one-dimensional time series to the trajectory matrix by means of a stay procedure (this gives the nominate to the whole technique); Singular Value Decomposition of the trajectory matrix; reconstruction of the original time series based on a numeral of selected eigenvectors. The result of the Caterpillar-SSA processing is a natural decomposition of the time series into several components, which pot often be identified as trends, seasonalities and other oscillatory series, or randomness components. The method pot be naturally extended to forecasting time series and its components, processing multidimensional time series and to change-point sensing. The "Caterpillar" ideas were independently developed in Russia (St. Petersburg, Moscow) and also in UK and USA (under the nominate of SSA; that is, Singular Spectrum Analysis). The new reserve "Analysis of Time Series Structure: SSA and Related Techniques", authors are N. Golyandina, V. Nekrutkin and A. Zhigljavsky, provides a careful, limpid description of SSA general theory and methodology (in English, Chapman&Hall / CRC, visualize http: / / www.gistatgroup.com / hombre / ). The method is a right and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and nonstationary, near deterministic and noisy time series are to be analyzed. We are sure, that in a close future "Caterpillar"-like methods will rate among the base methods of time series analysis and will be included in touchstone statistical software.
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