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Robustness of Time Series Forecasting Based on Regression Models

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Robustness in Statistical Forecasting

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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.

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Authors and Affiliations

  1. Department of Mathematical Modeling and Data Analysis, Belarusian State University, Minsk, Belarus

    Yuriy Kharin

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  1. Yuriy Kharin
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Kharin, Y. (2013). Robustness of Time Series Forecasting Based on Regression Models. In: Robustness in Statistical Forecasting. Springer, Cham. https://doi.org/10.1007/978-3-319-00840-0_6

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