Springer, 2013. — 368 p. — ISBN: 3319008390, 9783319008394Traditional 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.Contents:
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5th Edition. — Wiley, 2015. — 712 p. — ISBN: 978-1-118-67502-1.
Praise for the Fourth Edition "The book follows faithfully the style of the original edition. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control." - Mathematical Reviews Bridging classical models and modern...