Singular Spectrum Analysis
22,99 €
inkl. MwSt.

Versandfertig in über 4 Wochen
payback
11 °P sammeln
  • Broschiertes Buch

High Quality Content by WIKIPEDIA articles! 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…mehr

Andere Kunden interessierten sich auch für
Produktbeschreibung
High Quality Content by WIKIPEDIA articles! 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..