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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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Main Authors: Ercan, Ali. (Author, http://id.loc.gov/vocabulary/relators/aut), Kavvas, M. Levent. (http://id.loc.gov/vocabulary/relators/aut), Abbasov, Rovshan K. (http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2013.
Edition:1st ed. 2013.
Series:SpringerBriefs in Statistics,
Subjects:
Statistics .
Statistical physics.
Dynamical systems.
Climate change.
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Complex Systems.
Climate Change.
Statistical Physics and Dynamical Systems.
Online Access:https://doi.org/10.1007/978-3-319-01505-7
Table of Contents:
  • 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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