Long-Range Dependence and Sea Level Forecasting

This study shows that the Caspian Sea level time series possess long range dependence even after removing linear trends, based on analyses of the Hurst statistic, the sample autocorrelation functions, and the periodogram of the series. Forecasting performance of ARMA, ARIMA, ARFIMA and Trend Line-AR...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Ercan, Ali (Autor), Kavvas, M. Levent (Autor), Abbasov, Rovshan K. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Colección:SpringerBriefs in Statistics,
Temas:
Statistics .
System theory.
Environment.
Mathematical physics.
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Complex Systems.
Environmental Sciences.
Theoretical, Mathematical and Computational Physics.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 1. Introduction
  • 2. Long-Range Dependence and ARFIMA Models
  • 3. Forecasting, Confidence Band Estimation and Updating
  • 4.Case Study I: Caspian Sea Level
  • 5.Case Study II: Sea Level Change at Peninsular Malaysia and Sabah-Sarawak
  • 6. Summary and Conclusions
  • 7. References.

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