000			03539nam a22005297a 4500
001			sulb-eb0023382
003			BD-SySUS
005			20160413122348.0
007			cr nn 008mamaa
008			130829s2013 gw | s |||| 0|eng d
020			_a9783319015057 _9978-3-319-01505-7
024	7		_a10.1007/978-3-319-01505-7 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aPD _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aErcan, Ali. _eauthor.
245	1	0	_aLong-Range Dependence and Sea Level Forecasting _h[electronic resource] / _cby Ali Ercan, M. Levent Kavvas, Rovshan K. Abbasov.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2013.
300			_aV, 51 p. 21 illus., 6 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Statistics, _x2191-544X
505	0		_a1. 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.
520			_aThis 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.
650		0	_aStatistics.
650		0	_aStatistical physics.
650		0	_aDynamical systems.
650		0	_aClimate change.
650	1	4	_aStatistics.
650	2	4	_aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650	2	4	_aStatistical Physics, Dynamical Systems and Complexity.
650	2	4	_aClimate Change.
700	1		_aKavvas, M. Levent. _eauthor.
700	1		_aAbbasov, Rovshan K. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319015040
830		0	_aSpringerBriefs in Statistics, _x2191-544X
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-01505-7
912			_aZDB-2-SMA
942			_2Dewey Decimal Classification _ceBooks
999			_c45474 _d45474