Logo - springer
Slogan - springer

Statistics - Physical & Information Science | Long-Range Dependence and Sea Level Forecasting

Long-Range Dependence and Sea Level Forecasting

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

2013, V, 51 p. 21 illus., 6 illus. in color.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$39.99

(net) price for USA

ISBN 978-3-319-01505-7

digitally watermarked, no DRM

Included Format: PDF and EPUB

download immediately after purchase


learn more about Springer eBooks

add to marked items

Softcover
Information

Softcover (also known as softback) version.

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$54.99

(net) price for USA

ISBN 978-3-319-01504-0

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

  • ​A unique statistical approach to estimate sea level forecasts
  • Case studies included
  • Written by experts in the field

​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.

Content Level » Research

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

Related subjects » Complexity - Global Change - Climate Change - Physical & Information Science

Table of contents 

​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

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.