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This book examines robustness of time series forecasting. It evaluates sensitivity of the forecast risks to distortions and presents new robust forecasting procedures.

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This book offers solutions to such topical problems as developing mathematical models and descriptions of typical distortions in applied forecasting problems; evaluating robustness for traditional forecasting procedures under distortionism and more.

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Preface.- Symbols and Abbreviations.- Introduction.- A Decision-Theoretic Approach to Forecasting.- Time Series Models of Statistical Forecasting.- Performance and Robustness Characteristics in Statistical Forecasting.- Forecasting under Regression Models of Time Series.- Robustness of Time Series Forecasting Based on Regression Models.- Optimality and Robustness of ARIMA Forecasting.- Optimality and Robustness of Vector Autoregression Forecasting under Missing Values.- Robustness of Multivariate Time Series Forecasting Based on Systems of Simultaneous Equations.- Forecasting of Discrete Time Series.- Index.

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From the reviews:

"The book is intended for mathematicians, statisticians and software developers in applied mathematics, computer science, data analysis, and econometrics, among other topics. It is a good text for advanced undergraduate and postgraduate students of the mentioned disciplines." (Oscar Bustos, zbMATH, Vol. 1281, 2014)

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Yuriy Kharin is Chairman of the Department of Mathematical Modeling & Data Analysis, Director of the Research Institute for Applied Problems of Mathematics & Informatics at the Belarusian State University. He completed his Ph.D. in Math. Sci. at the Tomsk State University in 1974 and his Dr. Sci. in Math. Sci. at the USSR Academy of Sciences in 1986. His research interests include mathematical and applied statistics, robust statistics, and statistical forecasting. He is founder and first President of the Belarusian Statistical Association (1998), Laureate of National Science Prize (2002), and a Correspondent Member of the National Academy of Sciences of Belarus (2004).

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