Emphasizes basic methods for modeling linear dynamic systems. This book presents an understanding of basic concepts, such as multivariate random variables, stochastic processes, and regression-based methods. It covers topics that include spectral analysis, state space models, and recursive estimation.

"In this book the author gives a detailed account of estimation, identification methodologies for univariate and multivariate stationary time-series models. The interesting aspect of this introductory book is that it contains several real data sets and the author made an effort to explain and motivate the methodology with real data. this introductory book will be interesting and useful not only to undergraduate students in the UK universities but also to statisticians who are keen to learn time-series techniques and keen to apply them. I have no hesitation in recommending the book."Journal of Time Series Analysis, December 2009 "The book material is invaluable and presented with clarity it is strongly recommended to libraries and all who are interested in time series analysis."Hassan S. Bakouch, Tanta University, Journal of the Royal Statistical Society "Although the book is simply called Time Series Analysis, it is really a time series text for engineersand that is a good thing I see this text as a marble cake, mixing time series analysis and engineering in harmony, frosted with applications, and ready for students to gobble up."Joshua D. Kerr, California State UniversityEast Bay, Journal of the American Statistical Association, June 2009, Vol. 104, No. 486 "It is a very important and useful book which can be seen as a text for graduates in engineering or science departments, but also for statisticians who want to understand the link between models and methods for linear dynamical systems and linear stochastic processes."T. Postelnicu, Zentralblatt MATH, 2009

Autorentext

Henrik Madsen

Klappentext

Helping readers understand the relationship between linear dynamic systems and linear stochastic processes, Time Series Analysis presents an accessible yet thorough introduction to the methods for modeling linear stochastic systems. It links systems theory with time series theory and explores static and dynamic models to illustrate the differences between model types. The text also examines the significant topics of ARMA and ARIMA processes, the autocovariance function, and the Kalman filter. It provides numerous real-world examples from economics, physics, and engineering, several exercises at the end of most chapters, and a web page with computer-based exercises and a complete collection of slides.

Inhalt

Preface. Introduction. Multivariate Random Variables. Regression-Based Methods. Linear Dynamic Systems. Stochastic Processes. Identification, Estimation, and Model Checking. Spectral Analysis. Linear Systems and Stochastic Processes. Multivariate Time Series. State Space Models of Dynamic Systems. Recursive Estimation. Real Life Inspired Problems. Appendices. Bibliography. Index.