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Robustness Analysis in Forecasting of Time Series

In: Developments in Robust Statistics

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  • Y. Kharin

    (Belarussian State University)

Abstract

Summary 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.

Suggested Citation

  • Y. Kharin, 2003. "Robustness Analysis in Forecasting of Time Series," Springer Books, in: Rudolf Dutter & Peter Filzmoser & Ursula Gather & Peter J. Rousseeuw (ed.), Developments in Robust Statistics, pages 180-193, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-57338-5_15
    DOI: 10.1007/978-3-642-57338-5_15
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