Singular Spectrum Analysis: Time series, Multivariate Statistics, Dynamical Systems, Signal Processing, Random Fields, Takens' Theorem - Couverture souple

Couverture souple

ISBN 10 : 6130496303 ISBN 13 : 9786130496302

Editeur : Betascript Publishing, 2010

Synopsis

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Singular spectrum analysis (SSA) combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. Its roots lie in the classical Karhunen (1946)–Loève (1945, 1978) spectral decomposition of time series and random fields and in the Mañé (1981)–Takens (1981) embedding theorem. In practice, SSA is a nonparametric spectral estimation method based on embedding a time series X(t): t = 1,N in a vector space of dimension M. SSA proceeds by diagonalizing the Mtimes M lag-covariance matrix {textbf C}_X of X(t) to obtain spectral information on the time series, assumed to be stationary in the weak sense. The matrix {textbf C}_X can be estimated directly from the data as a Toeplitz matrix with constant diagonals (Vautard and Ghil, 1989), i.e..

Détails de l'édition

• Éditeur: Betascript Publishing
• Date d'édition: 2010
• Langue: anglais
• ISBN 10: 6130496303
• ISBN 13: 9786130496302
• Reliure: Broché
• Nombre de pages: 92
• Éditeur: Surhone Lambert M., Timpledon Miriam T., Marseken Susan F.
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible