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Singular Spectrum Analysis for Time SeriesSSA for Forecasting, Interpolation, Filtering and Estimation

Singular Spectrum Analysis for Time Series: SSA for Forecasting, Interpolation, Filtering and... [The applications of SSA dealt with in Chap. 3 require the use of models and hence SSA of Chap. 3 is mostly model-based. As the main model, the assumption that the components of the original time series, which are extracted by SSA, satisfy (at least, locally) certain linear recurrence relations. The main emphasis in Chap. 3 is on time series forecasting and different methods of checking stability and adequacy of forecasts. Other related problems such as imputation of missing values, interpolation and filtering are examined. Chapter 3 also surveys methods of parameter estimation of the models; such methods are very popular in signal processing. Chapter 3 concludes with descriptions of model-based extensions of multivariate and multidimensional SSA.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Singular Spectrum Analysis for Time SeriesSSA for Forecasting, Interpolation, Filtering and Estimation

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Publisher
Springer Berlin Heidelberg
Copyright
© The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature 2020
ISBN
978-3-662-62435-7
Pages
91 –146
DOI
10.1007/978-3-662-62436-4_3
Publisher site
See Chapter on Publisher Site

Abstract

[The applications of SSA dealt with in Chap. 3 require the use of models and hence SSA of Chap. 3 is mostly model-based. As the main model, the assumption that the components of the original time series, which are extracted by SSA, satisfy (at least, locally) certain linear recurrence relations. The main emphasis in Chap. 3 is on time series forecasting and different methods of checking stability and adequacy of forecasts. Other related problems such as imputation of missing values, interpolation and filtering are examined. Chapter 3 also surveys methods of parameter estimation of the models; such methods are very popular in signal processing. Chapter 3 concludes with descriptions of model-based extensions of multivariate and multidimensional SSA.]

Published: Nov 24, 2020

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