This book brings together all of the important new results on the state space framework for exponential smoothing.

The book presents methodologies for time series analysis in a simplified, example-based approach.

Robust versions of the exponential and Holt-Winters smoothing method for forecasting are presented.

From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods.

... probabilistic reasoning , includ- ing Bayesian modeling , can be found only in what it produces . Only when the ... example is the smoothing of partial autocorrelation coefficients . The second is the estimation of a smooth impulse ...

We study multiple aspects of robustness (in the setting of time series, image classification and linear regression) in this dissertation work. First three chapters concerns the time series setting.

The text begins with simple, single output systems, and proceeds to complex systems with multiple outputs and many inputs. This methodology works equally well for engineering designs, or process design, or process improvement.

Introduction and summary; Stochastic models and their forecasting; The autocorrelation function and spectrum; Linear stationary models; Linear nonstationary models; Forecasting; Stochastic model building; Model identification; Model ...

Accessible to undergraduates, this unique volume also offers complete guidelines valuable to researchers, practitioners, and advanced students in statistics, operations research, and engineering.