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Time Series Models

ISBN-13: 9783031132124 / Angielski / Miękka / 2022 / 201 str.

Manfred Deistler; Wolfgang Scherrer
Time Series Models Manfred Deistler Wolfgang Scherrer 9783031132124 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Time Series Models

ISBN-13: 9783031132124 / Angielski / Miękka / 2022 / 201 str.

Manfred Deistler; Wolfgang Scherrer
cena 361,42
(netto: 344,21 VAT:  5%)

Najniższa cena z 30 dni: 346,96
Termin realizacji zamówienia:
ok. 22 dni roboczych
Dostawa w 2026 r.

Darmowa dostawa!

This 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.

This 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.

Kategorie:
Nauka, Matematyka
Kategorie BISAC:
Mathematics > Probability & Statistics - Stochastic Processes
Business & Economics > Ekonometria
Technology & Engineering > Electronics - General
Wydawca:
Springer
Seria wydawnicza:
Lecture Notes in Statistics
Język:
Angielski
ISBN-13:
9783031132124
Rok wydania:
2022
Dostępne języki:
Numer serii:
000013119
Ilość stron:
201
Waga:
0.31 kg
Wymiary:
23.39 x 15.6 x 1.17
Oprawa:
Miękka
Dodatkowe informacje:
Wydanie ilustrowane

Preface.- 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.

Manfred Deistler is Emeritus Professor of Econometrics and System Theory at the Institute of Statistics and Mathematical Methods in Economics at the TU Wien, Vienna, Austria. His research interests include time series analysis, systems identification and econometrics. He is a Fellow of the Econometric Society, the IEEE, and the Journal of Econometrics.

Wolfgang Scherrer is a Professor of Econometrics and System Theory at the Institute of Statistics and Mathematical Methods in Economics at the TU Wien, Vienna, Austria. His research interests include time series analysis, econometrics, dynamic factor models and applications in the area of energy supply.


This 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.



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