The program is based on the powerful model-free method of time series analysis Caterpillar-SSA (Singular Spectrum Analysis). The result of the Caterpillar-SSA processing is identification, analysis and forecast of additive components of time series (trends, periodicities, noise). The program can be applied to multivariate analysis/forecasting and change-point detection.
The program is based on the powerful model-free method of time series analysis Caterpillar (another name 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 delay procedure (this gives the name to the whole technique); Singular Value Decomposition of the trajectory matrix; reconstruction of the original time series based on a number of selected eigenvectors. The result of the Caterpillar-SSA processing is a natural decomposition of the time series into several components, which can often be identified as trends, seasonalities and other oscillatory series, or noise components. The method can be naturally extended to forecasting time series and its components, processing multidimensional time series and to change-point detection. The "Caterpillar" ideas were independently developed in Russia (St. Petersburg, Moscow) and also in UK and USA (under the name of SSA; that is, Singular Spectrum Analysis). The new book "Analysis of Time Series Structure: SSA and Related Techniques", authors are N. Golyandina, V. Nekrutkin and A. Zhigljavsky, provides a careful, lucid description of SSA general theory and methodology (in English, Chapman&Hall/CRC, see http://www.gistatgroup.com/cat/). The method is a powerful 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, almost deterministic and noisy time series are to be analyzed. We are sure, that in a near future "Caterpillar"-like methods will rank among the base methods of time series analysis and will be included in standard statistical software.
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