LEADER 04053nam 22007935 450 001 9910620195703321 005 20251118142448.0 010 $a3-031-13213-0 024 7 $a10.1007/978-3-031-13213-1 035 $a(MiAaPQ)EBC7120668 035 $a(Au-PeEL)EBL7120668 035 $a(CKB)25188927200041 035 $a(PPN)265856434 035 $a(DE-He213)978-3-031-13213-1 035 $a(EXLCZ)9925188927200041 100 $a20221021d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTime Series Models /$fby Manfred Deistler, Wolfgang Scherrer 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (213 pages) 225 1 $aLecture Notes in Statistics,$x2197-7186 ;$v224 300 $aIncludes index. 311 08$aPrint version: Deistler, Manfred Time Series Models Cham : Springer International Publishing AG,c2022 9783031132124 327 $aPreface -- 1 Time Series and Stationary Processes -- 2 Prediction -- 3 Spectral Representation -- 4 Filter -- 5 Autoregressive Processes -- 6 ARMA Systems and ARMA Processes -- 7 State-Space Systems -- 8 Models with Exogenous Variables -- 9 Granger Causality -- 10 Dynamic Factor Models -- 10 ARCH and GARCH Models -- Index. 330 $aThis textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary processes and linear dynamic models, it describes the basic concepts, ideas, methods and results in a mathematically well-founded form and includes numerous examples and exercises. The first part presents the theory of weakly stationary processes in time and frequency domain, including prediction and filtering. The second part deals with multivariate AR, ARMA and state space models, which are the most important model classes for stationary processes, and addresses the structure of AR, ARMA and state space systems, Yule-Walker equations, factorization of rational spectral densities and Kalman filtering. Finally, there is a discussion of Granger causality, linear dynamic factor models and (G)ARCH models. The book provides a solid basis for advanced mathematics students and researchers in fields such as data-driven modeling, forecasting and filtering, which are important in statistics, control engineering, financial mathematics, econometrics and signal processing, among other subjects. 410 0$aLecture Notes in Statistics,$x2197-7186 ;$v224 606 $aTime-series analysis 606 $aStochastic processes 606 $aEconometrics 606 $aStatistics 606 $aStatistics 606 $aSignal processing 606 $aTime Series Analysis 606 $aStochastic Processes 606 $aEconometrics 606 $aStatistical Theory and Methods 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aSignal, Speech and Image Processing 606 $aAnàlisi de sèries temporals$2thub 608 $aLlibres electrònics$2thub 615 0$aTime-series analysis. 615 0$aStochastic processes. 615 0$aEconometrics. 615 0$aStatistics. 615 0$aStatistics. 615 0$aSignal processing. 615 14$aTime Series Analysis. 615 24$aStochastic Processes. 615 24$aEconometrics. 615 24$aStatistical Theory and Methods. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aSignal, Speech and Image Processing. 615 7$aAnàlisi de sèries temporals 676 $a519.55 676 $a519.55 700 $aDeistler$b M$g(Manfred),$021011 702 $aScherrer$b Wolfgang 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910620195703321 996 $aTime Series Models$92960587 997 $aUNINA