Robustness in Statistical Forecasting Softcover reprint of the original 1st ed. 2013 Edition

From the reviews:

“The book is intended for mathematicians, statisticians and software developers in applied mathematics, computer science, data analysis, and econometrics, among other topics. It is a good text for advanced undergraduate and postgraduate students of the mentioned disciplines.” (Oscar Bustos, zbMATH, Vol. 1281, 2014)

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.