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.
The first book with a specific focus on robustness of time series forecasting
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.
This book examines robustness of time series forecasting. It evaluates sensitivity of the forecast risks to distortions and presents new robust forecasting procedures.