This book examines robustness of time series forecasting. It evaluates sensitivity of the forecast risks to distortions and presents new robust forecasting procedures.
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Yuriy Kharin is Chairman of the Department of Mathematical Modeling & Data Analysis, Director of the Research Institute for Applied Problems of Mathematics & Informatics at the Belarusian State University. He completed his Ph.D. in Math. Sci. at the Tomsk State University in 1974 and his Dr. Sci. in Math. Sci. at the USSR Academy of Sciences in 1986. His research interests include mathematical and applied statistics, robust statistics, and statistical forecasting. He is founder and first President of the Belarusian Statistical Association (1998), Laureate of National Science Prize (2002), and a Correspondent Member of the National Academy of Sciences of Belarus (2004).
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.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The first book with a specific focus on robustness of time series forecasting Evaluates sensitivity of the forecast risks to distortions and presents new robust forecasting procedures Presentation of the material follows the pattern model . Seller Inventory # 385703719
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 372 pp. Englisch. Seller Inventory # 9783319345680
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783319345680