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

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

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

... 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 ...

The book is also an excellent textbook for beginning graduate-level courses in advanced statistics, mathematics, economics, finance, engineering, and physics.

The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases.

The text begins with a consideration of data sources and sampling intervals, progressing to discussions of time series models and probability models.