| 书目名称 | Long-Range Dependence and Sea Level Forecasting |
| 编辑 | Ali Ercan,M. Levent Kavvas,Rovshan K. Abbasov |
| 视频video | |
| 概述 | A unique statistical approach to estimate sea level forecasts.Case studies included.Written by experts in the field |
| 丛书名称 | SpringerBriefs in Statistics |
| 图书封面 |  |
| 描述 | .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 usef |
| 出版日期 | Book 2013 |
| 关键词 | ARFIMA models; Sea level change; climate change; confidence interval estimation; forecast updating; long- |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-01505-7 |
| isbn_softcover | 978-3-319-01504-0 |
| isbn_ebook | 978-3-319-01505-7Series ISSN 2191-544X Series E-ISSN 2191-5458 |
| issn_series | 2191-544X |
| copyright | The Author(s) 2013 |
发表于 2025-3-21 19:09:13
