TY  - BOOK
AU  - Ercan,Ali
AU  - Kavvas,M.Levent
AU  - Abbasov,Rovshan K.
ED  - SpringerLink (Online service)
TI  - Long-Range Dependence and Sea Level Forecasting
T2  - SpringerBriefs in Statistics,
SN  - 9783319015057
AV  - QA276-280 
U1  - 519.5 23
PY  - 2013///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Statistics
KW  - Statistical physics
KW  - Dynamical systems
KW  - Climate change
KW  - Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
KW  - Statistical Physics, Dynamical Systems and Complexity
KW  - Climate Change
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-319-01505-7
ER  -