Nonstationarities in hydrologic and environmental time series [Libro electrónico] / A. Ramachandra Rao, Khaled H. Hamed and Huey-Long Chen
Por: Ramachandra Rao, A [autor/a].
Hamed, Khaled H [autor/a] | Chen, Huey-Long [autor/a].
Tipo de material: Libro en línea Series Editor: Dordrecht: Springer, c2003Descripción: xxvii, 365 páginas : ilustraciones ; centímetros.ISBN: 9401039798; 9789401039796; 9789401001175 (Online).Nota de acceso: Disponible para usuarios de ECOSUR con su clave de acceso Nota de bibliografía: Incluye bibliografía: páginas 347-357 e índice: páginas 58-362 Número de sistema: 56949Resumen:Tipo de ítem | Biblioteca actual | Colección | Signatura | Estado | Fecha de vencimiento | Código de barras |
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Libros | Biblioteca Electrónica Recursos en línea (RE) | Acervo General | Recurso digital | ECO400569495755 |
Incluye bibliografía: páginas 347-357 e índice: páginas 58-362
Disponible para usuarios de ECOSUR con su clave de acceso
Conventionally, time series have been studied either in the time domain or the frequency domain. The representation of a signal in the time domain is localized in time, i.e . the value of the signal at each instant in time is well defined . However, the time representation of a signal is poorly localized in frequency , i.e. little information about the frequency content of the signal at a certain frequency can be known by looking at the signal in the time domain . On the other hand, the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time. In studying stationary or conditionally stationary processes with mixed spectra , the separate use of time domain and frequency domain analyses is sufficient to reveal the structure of the process . Results discussed in the previous chapters suggest that the time series analyzed in this book are conditionally stationary processes with mixed spectra. Additionally, there is some indication of nonstationarity, especially in longer time series. eng
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