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03508nam a22005175i 4500 |
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978-3-319-01505-7 |
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DE-He213 |
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20151103121256.0 |
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cr nn 008mamaa |
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130829s2013 gw | s |||| 0|eng d |
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|a 9783319015057
|9 978-3-319-01505-7
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|a 10.1007/978-3-319-01505-7
|2 doi
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|a Ercan, Ali.
|e author.
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|a Long-Range Dependence and Sea Level Forecasting
|h [electronic resource] /
|c by Ali Ercan, M. Levent Kavvas, Rovshan K. Abbasov.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2013.
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|a V, 51 p. 21 illus., 6 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|a text file
|b PDF
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|a SpringerBriefs in Statistics,
|x 2191-544X
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|a 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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|a 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.
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| 650 |
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|a Statistics.
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|a Statistical physics.
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|a Dynamical systems.
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|a Climate change.
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|a Statistics.
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|a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
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|a Statistical Physics, Dynamical Systems and Complexity.
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| 650 |
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|a Climate Change.
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| 700 |
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|a Kavvas, M. Levent.
|e author.
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| 700 |
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|a Abbasov, Rovshan K.
|e author.
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| 710 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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| 776 |
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|i Printed edition:
|z 9783319015040
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| 830 |
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|a SpringerBriefs in Statistics,
|x 2191-544X
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/978-3-319-01505-7
|z Full Text via HEAL-Link
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|a ZDB-2-SMA
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| 950 |
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|a Mathematics and Statistics (Springer-11649)
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