LEADER 05031nam 2200649Ia 450 001 9910876772903321 005 20200520144314.0 010 $a1-282-30785-1 010 $a9786612307850 010 $a0-470-31656-X 010 $a0-470-31727-2 035 $a(CKB)1000000000687558 035 $a(EBL)469783 035 $a(OCoLC)264615241 035 $a(SSID)ssj0000337794 035 $a(PQKBManifestationID)11276869 035 $a(PQKBTitleCode)TC0000337794 035 $a(PQKBWorkID)10293861 035 $a(PQKB)11780383 035 $a(MiAaPQ)EBC469783 035 $a(PPN)159354552 035 $a(EXLCZ)991000000000687558 100 $a19830210d1983 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aForecasting with univariate Box-Jenkins models $econcepts and cases /$fAlan Pankratz 210 $aNew York $cWiley$dc1983 215 $a1 online resource (587 p.) 225 0 $aWiley series in probability and mathematical statistics. Probability and mathematical statistics.,$x0271-6356 300 $aDescription based upon print version of record. 311 $a0-471-09023-9 320 $aIncludes bibliography and index. 327 $aForecasting With Univariate Box- Jenkins Models CONCEPTS AND CASES; CONTENTS; PART I. BASIC CONCEPTS; 1 Overview; 1.1 Planning and forecasting; 1.2 What this book is about; 1.3 Time-series data; 1.4 Single-series (univariate) analysis; 1.5 When may UBJ models be used?; 1.6 The Box-Jenkins modeling procedure; 1.7 UBJ models compared with other models; Summary; Questions and problems; 2 Introduction to Box-Jenkins analysis of a single data series; 2.1 Differencing; 2.2 Deviations from the mean 327 $a2.3 Two analytical tools: the estimated autocorrelation function (acf) and estimated partial autocorrelation function (pacf)Summary; Questions and problems; 3 Underlying statistical principles; 3.1 Process, realization, and model; 3.2 Two common processes; 3.3 Statistical inference at the identification stage; Summary; Appendix 3 A: Expected value rules and definitions; Questions and problems; 4 An introduction to the practice of ARIMA modeling; 4.1 What is a good model?; 4.2 Two examples of UBJ-ARIMA modeling; Summary; Questions and problems; 5 Notation and the interpretation of ARIMA models 327 $a5.1 Three processes and ARIMA (p,d,q) notation5.2 Backshift notation; 5.3 Interpreting ARIMA models I: optimal extrapolation of past values of a single series; 5.4 Interpreting ARIMA models II: rationalizing them from their context; 5.5 Interpreting ARIMA models III: ARIMA(O,d,q) models as exponentially weighted moving averages; Summary; Questions and problems; 6 Identification: stationary models; 6.1 Theoretical acfs and pacf's for five common processes; 6.2 Stationarity; 6.3 Invertibility; 6.4 Deriving theoretical acf's for the MA(1) process 327 $a6.5 Deriving theoretical acf's for the AR(1) processSummary; Appendix 6A: The formal conditions for stationarity and invertibility; Appendix 6B Invertibility, uniqueness,and forecast performance; Questions and problems; 7 Identification: nonstationary models; 7.1 Nonstationary mean; 7.2 Nonstationary variance; 7.3 Differencing and deterministic trends; Summary; Appendix 7A: Integration; 8 Estimation; 8.1 Principles of estimation; 8.2 Nonlinear least-squares estimation; 8.3 Estimation-stage results: have we found a good model?; Summary; Appendix 8A: Marquardt's compromise; 8A.1 Overview 327 $a8A.2 Application to an MA(1)Appendix 8B: Backcasting; 8B.1 Conditional least squares; 8B.2 Unconditional least squares; 9 Diagnostic checking; 9.1 Are the random shocks independent?; 9.2 Other diagnostic checks; 9.3 Reformulating a model; Summary; Questions and problems; 10 Forecasting; 10.1 The algebra of ARIMA forecasts; 10.2 The dispersion of ARIMA forecasts; 10.3 Forecasting from data in logarithmic form; 10.4 The optimality of ARIMA forecasts; Summary; Appendix 10A:The complementarity of ARIMA models and econometric models; Questions and problems; 11 Seasonal and other periodic models 327 $a11.1 Periodic data 330 $aExplains the concepts and use of univariate Box-Jenkins/ARIMA analysis and forecasting through 15 case studies. Cases show how to build good ARIMA models in a step-by-step manner using real data. Also includes examples of model misspecification. Provides guidance to alternative models and discusses reasons for choosing one over another. 410 0$aWiley Series in Probability and Statistics 606 $aTime-series analysis 606 $aPrediction theory 615 0$aTime-series analysis. 615 0$aPrediction theory. 676 $a519.54 676 $a519.55 700 $aPankratz$b Alan$f1944-$089085 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910876772903321 996 $aForecasting with univariate box-Jenkins models$9196473 997 $aUNINA LEADER 01872nam2 2200337 i 450 001 VAN00119038 005 20240806100803.337 010 $a978-88-506-5244-0 100 $a20181221d2018 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 0 $a3$fdi Sandro Pignatti$gFlora digitale$fdi Riccardo Guarino e Marco La Rosa 205 $a2. ed 210 $aBologna$cEdagricole$d2018 215 $aXIX, 1287 p.$cill.$d27 cm 461 1$1001VAN00117265$12001 $aFlora d'Italia$ein 4 volumi$fdi Sandro Pignatti$gFlora digitale$fdi Riccardo Guarino e Marco La Rosa$1205 $a2. ed$1210 $aBologna$cEdagricole$1215 $avolumi$d27 cm$v3 620 $dBologna$3VANL000003 676 $a581.945$cPiante - Italia$v22 700 1$aPignatti$bSandro$3VANV011450$011139 702 1$aGuarino$bRiccardo$3VANV090423 702 1$aLa Rosa$bMarco$3VANV090424 712 $aEdagricole $3VANV108178$4650 801 $aIT$bSOL$c20240906$gRICA 912 $aVAN00119038 950 $aBIBLIOTECA DEL DIPARTIMENTO DI SCIENZE E TECNOLOGIE AMBIENTALI BIOLOGICHE E FARMACEUTICHE$d17CONS Jb146 $e17BIB2818 20181221 $sBuono 950 $aBIBLIOTECA DEL DIPARTIMENTO DI SCIENZE E TECNOLOGIE AMBIENTALI BIOLOGICHE E FARMACEUTICHE$d17PREST Jb150 $e17BIB2019 20181221 $sBuono 950 $aBIBLIOTECA DEL DIPARTIMENTO DI SCIENZE E TECNOLOGIE AMBIENTALI BIOLOGICHE E FARMACEUTICHE$d17PREST Jb154 $e17BIB2020 20181221 $sBuono 950 $aBIBLIOTECA DEL DIPARTIMENTO DI SCIENZE E TECNOLOGIE AMBIENTALI BIOLOGICHE E FARMACEUTICHE$d17PREST Jb158 $e17BIB2021 20181221 $sBuono 996 $a3$93091036 997 $aUNICAMPANIA