Robustness in Statistical Forecasting

  • Yuriy Kharin

About this book

Introduction

Traditional procedures in the statistical forecasting of time series, which are proved to be optimal under the hypothetical model, are often not robust under relatively small distortions (misspecification, outliers, missing values, etc.), leading to actual forecast risks (mean square errors of prediction) that are much higher than the theoretical values. This monograph fills a gap in the literature on robustness in statistical forecasting, offering solutions to the following topical problems:

- developing mathematical models and descriptions of typical distortions in applied forecasting problems;

- evaluating the robustness for traditional forecasting procedures under distortions;

- obtaining the maximal distortion levels that allow the “safe” use of the traditional forecasting algorithms;

- creating new robust forecasting procedures to arrive at risks that are less sensitive to definite distortion types.      

Keywords

62-02, 62M20, 62M10, 62G35, 62-07, 62F35, 62C20, 62P20 forecasting model distortion risk robustness time series

Authors and affiliations

  • Yuriy Kharin
    • 1
  1. 1.Department of Mathematical Modeling and Data AnalysisBelarusian State UniversityMinskBelarus

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-00840-0
  • Copyright Information Springer International Publishing Switzerland 2013
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-00839-4
  • Online ISBN 978-3-319-00840-0
  • About this book