An Introduction to Time Series Modeling - Couverture souple

Jakobsson, Andreas

 
9789144108360: An Introduction to Time Series Modeling
Couverture souple
ISBN 10 : 9144108362 ISBN 13 : 9789144108360
Editeur : Studentlitteratur AB, 2015

Synopsis

Time series analysis concerns the mathematical modeling of time varying phenomena, e.g., ocean waves, water levels in lakes and rivers, demand for electrical power, radar signals, muscular reactions, ECG-signals, or option prices at the stock market. This book gives a comprehensive presentation of stochastic models and methods in time series analysis. The book treats stochastic vectors and both univariate and multivariate stochastic processes, as well as how these can be used to identify suitable models for various forms of observations. Furthermore, different approaches such as least squares, the prediction error method, and maximum likelihood are treated in detail, together with results on the Cramér-Rao lower bound, dictating the theoretically possible estimation accuracy. Residual analysis and prediction of stochastic models are also treated, as well as how one may form time-varying models, including the recursive least squares and the Kalman filter. The book discusses how to implement the various methods using Matlab, and several Matlab functions and data sets are provided with the book. The book provides an introduction to time series modeling of various forms of measurements, focusing on how such models may be identified and detailed. It has a practical approach, and include several examples illustrating the theory. The book is aimed at advanced undergraduate and junior graduate students in statistics, mathematics, or engineering. Helpful prerequisites include courses in multivariate analysis, linear systems, basic probability, and stochastic processes.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Time series analysis concerns the mathematical modeling of time varying phenomena, e.g., ocean waves, water levels in lakes and rivers, demand for electrical power, radar signals, muscular reactions, ECG-signals, or option prices at the stock market. This book gives a comprehensive presentation of stochastic models and methods in time series analysis. The book treats stochastic vectors and both univariate and multivariate stochastic processes, as well as how these can be used to identify suitable models for various forms of observations. Furthermore, different approaches such as least squares, the prediction error method, and maximum likelihood are treated in detail, together with results on the Cramér-Rao lower bound, dictating the theoretically possible estimation accuracy. Residual analysis and prediction of stochastic models are also treated, as well as how one may form time-varying models, including the recursive least squares and the Kalman filter. The book discusses how to implement the various methods using Matlab, and several Matlab functions and data sets are provided with the book. The book provides an introduction to time series modeling of various forms of measurements, focusing on how such models may be identified and detailed. It has a practical approach, and include several examples illustrating the theory. The book is aimed at advanced undergraduate and junior graduate students in statistics, mathematics, or engineering. Helpful prerequisites include courses in multivariate analysis, linear systems, basic probability, and stochastic processes.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Studentlitteratur AB
Date d'édition
2015
Langue
anglais
ISBN 10 
9144108362
ISBN 13 
9789144108360
Reliure
Broché
Nombre de pages
387
Coordonnées du fabricant
non disponible
Personne responsable
non disponible

Autres éditions populaires du même titre

9789144083742: Introduction to Time Series Modeling

Edition présentée

ISBN 10 :  9144083742 ISBN 13 :  9789144083742
Editeur : Studentlitteratur AB, 2013
Couverture souple