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-ARFIMA (TL-ARFIMA) combination models are investigated. The forecast confidence bands and the forecast updating methodology, provided for ARIMA models in the literature, are modified for the ARFIMA models. Sample autocorrelation functions are utilized to estimate the differencing lengths of the ARFIMA models. The confidence bands of the forecasts are estimated using the probability densities of the residuals without assuming a known distribution.
There are no long-term sea level records for the region of Peninsular Malaysia and Malaysia's Sabah-Sarawak northern region of Borneo Island. In such cases the Global Climate Model (GCM) projections for the 21st century can be downscaled to the Malaysia region by means of regression techniques, utilizing the short records of satellite altimeters in this region against the GCM projections during a mutual observation period.
This book will be useful for engineers and researchers working in the areas of applied statistics, climate change, sea level change, time series analysis, applied earth sciences, and nonlinear dynamics.

Sommario

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

Dettagli sul prodotto

• Autori: Rovshan K. Abbasov, Ali Ercan, M. Levent Kavvas
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 12.09.2013
• EAN: 9783319015040
• ISBN: 978-3-31-901504-0
• Pagine: 51
• Dimensioni: 154 mm x 239 mm x 5 mm
• Peso: 116 g
• Illustrazioni: V, 51 p. 21 illus., 6 illus. in color.
• Serie: SpringerBriefs in Statistics
• Categorie: Scienze naturali, medicina, informatica, tecnica > Matematica > Teoria delle probabilità, stocastica, statistica matematica; C, Climate Change, Statistics, The environment, Mathematics and Statistics, Complex systems, Theoretical, Mathematical and Computational Physics, Environmental Sciences, Dynamical systems, Dynamics & statics, Statistical physics, Statistical Physics and Dynamical Systems