Long-Range Dependence and Sea Level Forecasting

  • Ali Ercan
  • M. Levent Kavvas
  • Rovshan K. Abbasov

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Table of contents

  1. Front Matter
    Pages i-v
  2. Ali Ercan, M. Levent Kavvas, Rovshan K. Abbasov
    Pages 1-5
  3. Ali Ercan, M. Levent Kavvas, Rovshan K. Abbasov
    Pages 7-10
  4. Ali Ercan, M. Levent Kavvas, Rovshan K. Abbasov
    Pages 11-14
  5. Ali Ercan, M. Levent Kavvas, Rovshan K. Abbasov
    Pages 15-37
  6. Ali Ercan, M. Levent Kavvas, Rovshan K. Abbasov
    Pages 39-48
  7. Ali Ercan, M. Levent Kavvas, Rovshan K. Abbasov
    Pages 49-51

About this book

Introduction

​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.

Keywords

ARFIMA models Sea level change climate change confidence interval estimation forecast updating long-range dependence

Authors and affiliations

  • Ali Ercan
    • 1
  • M. Levent Kavvas
    • 2
  • Rovshan K. Abbasov
    • 3
  1. 1.Department of Civil and Environmental EnUniversity of CaliforniaDavisUSA
  2. 2.Department of Civil and Environmental EnUniversity of CaliforniaDavisUSA
  3. 3.Khazar UniversityBakuAzerbaijan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-01505-7
  • Copyright Information The Author(s) 2013
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-01504-0
  • Online ISBN 978-3-319-01505-7
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • About this book