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.
