Applied Time Series Analysis contains the proceedings of the First Applied Time Series Symposium held in Tulsa, Oklahoma, on May 14-15, 1976. The symposium provided a forum for reviewing various applications of time series analysis and covered topics ranging from nonlinear time series modeling and G-spectral estimation to multivariate autoregression estimation using residuals. Adaptive processing of seismic data and the application of homomorphic filtering to seismic data processing are also discussed. Comprised of 10 chapters, this book begins by describing the application of parametric models to the analysis and control of time series using some numerical examples. The reader is then introduced to nonlinear time series modeling; two-dimensional recursive filtering in theory and practice; and spectral estimators. Waves propagating in random media as statistical time series are also considered. The book concludes with a chapter that illustrates how the intensity of a Poisson process is estimated, with emphasis on a time series approach to the fixed signal case, invariant testing, and spline estimation. This monograph will be a useful resource for students and practitioners in the fields of mathematics and statistics, electrical engineering, and computer science.