000			02399cam a22004217i 4500
001			20791080
003			VITAP
005			20250425155616.0
008			181226t20192019enka b 001 0 eng d
010			_a 2018968317
015			_aGBB918872 _2bnb
016	7		_a019226600 _2Uk
020			_a9780128131176
020			_a0128131179
020			_z9780128131183 _q(ePub ebook)
035			_a(OCoLC)on1057646417
040			_aYDX _beng _cVITAP _erda _dBDX _dOCLCQ _dUKMGB _dOCLCO _dOCLCF _dZWU _dOCLCQ _dDLC
042			_alccopycat
050	0	0	_aQA280 _b.M544 2019
082	0	4	_a519.55 MIL _223
100	1		_aMills, Terence C., _eauthor.
245	1	0	_aApplied Time Series Analysis : _bA Practical Guide to Modeling And Forecasting / _cTerence C. Mills.
264		1	_aLondon : _bAcademic Press, _c[2019]
264		4	_c©2019
300			_axiii, 339 pages : _billustrations ; _c23 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_aIt include index pages
504			_aIncludes bibliographical references (pages 315-327) and index
505	0		_aTime series and their features -- Transforming time series -- ARMA models for stationary time series -- ARIMA models for nonstationary time series -- Unit roots, difference and trend stationarity, and fractional differencing -- Breaking and nonlinear trends -- An introduction to forecasting with univariate models -- Unobserved component models, signal extraction, and filters -- Seasonality and exponential smoothing -- Volatility and generalized autoregressive conditional heteroskedastic processes -- Nonlinear stochastic processes -- Transfer functions and autoregressive distributed lag modeling -- Vector autoregressions and Granger causality -- Error corection, spurious regressions, and cointegration -- Vector autoregressions with integrated variables, vector error correction models, and common trends -- Compositional and count time series -- State space models.
650		0	_aTime-series analysis.
650		7	_aTime-series analysis. _2fast _0(OCoLC)fst01151190
776	0	8	_iEbook version : _z9780128131183
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cREF
999			_c46808 _d46808