Singular Spectrum Analysis for Time Series
Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting combining elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA seeks to decompose the original series into a sum of a small numb...
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Livre numérique |
| Sprog: | Anglais |
| Udgivet: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Udgivelse: | 1st ed. 2013. |
| Serier: | SpringerBriefs in Statistics
|
| Online adgang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Kommentar: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Singular Spectrum Analysis for Time Series, Texte imprimé, 9783642349140 • Singular spectrum analysis for time series, Nina Golyandina, Anatoly Zhigljavsky, 2013, Heidelberg [etc.], Springer, 1 vol. (VII-119 p.), Springer briefs in statistics, 978-3-642-34912-6 |
Indholdsfortegnelse:
- Introduction: Preliminaries SSA Methodology and the Structure of the Book SSA Topics Outside the Scope of this Book Common Symbols and Acronyms Basic SSA: The Main Algorithm Potential of Basic SSA Models of Time Series and SSA Objectives Choice of Parameters in Basic SSA Some Variations of Basic SSA SSA for Forecasting, interpolation, Filtration and Estimation: SSA Forecasting Algorithms LRR and Associated Characteristic Polynomials Recurrent Forecasting as Approximate Continuation Confidence Bounds for the Forecast Summary and Recommendations on Forecasting Parameters Case Study: Fortified Wine Missing Value Imputation Subspace-Based Methods and Estimation of Signal Parameters SSA and Filters