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Robustness of Time Series Forecasting Based on Regression Models

In: Robustness in Statistical Forecasting

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  • Yuriy Kharin

    (Belarusian State University, Department of Mathematical Modeling and Data Analysis)

Abstract

This chapter presents a robustness analysis of the forecasting statistics introduced in the previous chapter under the following distortion types: four functional distortion varieties of the regression function, additive outliers, and correlation between random errors. A quantitative characterization of forecasting robustness is obtained by using the robustness indicators introduced in Chap. 4 , namely the forecast risk instability coefficient and the δ-admissible distortion level. Robust forecasting statistics are constructed by using Huber estimators and a specially chosen type of M-estimators for the regression function parameters. A local-median forecasting algorithm is proposed to mitigate the influence of outliers under regression models, and its robustness is evaluated.

Suggested Citation

  • Yuriy Kharin, 2013. "Robustness of Time Series Forecasting Based on Regression Models," Springer Books, in: Robustness in Statistical Forecasting, edition 127, chapter 0, pages 105-162, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-00840-0_6
    DOI: 10.1007/978-3-319-00840-0_6
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