Robustness analysis in forecasting of time series

The problems of statistical forecasting of time series under distortions of traditional hypothetical models are considered. The following distorted models of time series are used: trend models under "outliers" and functional distortions, regression models under "outliers" and "errors-in-regressors", autoregressive time series with parameter specification errors and non-homogeneous innovations. Robustness characteristics based on the mean square risk of forecasting are introduced and evaluated for these cases. In addition, new robust forecasting procedures are presented.