Robustness of Time Series Forecasting Based on Regression Models

Yuriy Kharin
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Yuriy Kharin: Belarusian State University, Department of Mathematical Modeling and Data Analysis

Chapter Chapter 6 in Robustness in Statistical Forecasting, 2013, pp 105-162 from Springer

Abstract: 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.

Keywords: Risk Forecasting; Distortion Types; Distortion Level; Huber Estimator; Statistical Forecasting (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-00840-0_6

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DOI: 10.1007/978-3-319-00840-0_6

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