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Forecasting is fascinating. Who wouldn't like to cast a glimpse into the future? Far removed from metaphysics, mathematical methods such as time-lapse techniques, time series or arti?cial neural netwoks o?er a rational means of achieving this. A precondition for the latter is the availability of a sequence of observed values from the past whose temporal classi?cation permits the deduction of attributes necessary for forecasting purposes. The subject matter of this book is uncertain forecasting using time series and neural networks based on uncertain observed data. 'Uncertain' data - plies information exhibiting inaccuracy, uncertainty and questionability. The uncertainty of individual observations is modeled in this book by fuzziness. Sequences of uncertain observations hence constitute fuzzy time series. By means of new discretization techniques for uncertain data it is now possible to correctly and completely retain data uncertainty in forecasting work. The book presents numerical methods which permit successful forecasting not only in engineering but also in many other ?elds such as environmental science or economics, assuming of course that a suitable sequence of observed data is available. By taking account of data uncertainty, the indiscriminate reduction of uncertain observations to real numbers is avoided. The larger information content described by uncertainty is retained, and compared with real data, provides a deeper insight into causal relationships. This in turn has practical consequences as far as the full?lment of technical requirements in engineering applications is concerned.
List of contents
Mathematical Description of Uncertain Data.- Analysis of Time Series Comprised of Uncertain Data.- Forecasting of Time Series Comprised of Uncertain Data.- Uncertain Forecasting in Engineering and Environmental Science.
About the author
Dr. Bernd Möller promovierte am Institut für Betriebswirtschaftliche Risikoforschung und Versicherungswirtschaft (Prof. Dr. Elmar Helten) der Universität München. Er ist derzeit im Konzern-Controlling eines deutschen Versicherungskonzerns tätig.
Dr. med. Uwe Reuter, geboren 1961 in Zwickau, verheiratet, 2 Kinder; bis 1986 Medizinstudium an Universität Greifswald, Abschluss mit Promotion; bis 1991 Weiterbildung zum FA für Orthopädie an den Kliniken Löbau, Zwickau, München und Eisenberg; seit 1986 Beschäftigung mit den Methoden der Reflexmedizin; seit 1993 niedergelassen als homöopathischer Arzt und fachübergreifender Gemeinschaftspraxis in Greiz/Thüringisches Vogtland; Zusatzbezeichnung "Homöopathie", "Chirotherapie", "spezielle Schmerztherapie" und "Naturheilverfahren"; seit 1993 Weiterbildungsleiter für Homöopathie in Thüringen; seit 1996 Algesiologe/STK und anerkannter Schmerztherapeut der KV; 1997 Gastdozent zur Ringvorlesung "Naturheilverfahren" an der Universität Dresden; seit 1998 diplomierter Fastenarzt nach F.X. MAYR; Inaugurator der Procain-Basen-Infusion und der Thermokochsalztherapie; Mitgründer der Fachakademie für Ganzheitliche Medizin (heute: ProLeben Akademie); Mitgründer des Institutes für innovative Medizin, Forschung und Kommunikation; Ärztlicher Direktor der Klinik und Praxis ProLeben, Fachbehandlungszentrum für Biologische Tumorabwehr, Ernährungstherapie, Naturheilverfahren, spezielle Schmerztherapie und Homöopathie; 2001 Mitbegründer des ProLeben-Medizin Verbundes für Biologische Medizin
Summary
Forecasting is fascinating. Who wouldn’t like to cast a glimpse into the future? Far removed from metaphysics, mathematical methods such as time-lapse techniques, time series or arti?cial neural netwoks o?er a rational means of achieving this. A precondition for the latter is the availability of a sequence of observed values from the past whose temporal classi?cation permits the deduction of attributes necessary for forecasting purposes. The subject matter of this book is uncertain forecasting using time series and neural networks based on uncertain observed data. ‘Uncertain’ data - plies information exhibiting inaccuracy, uncertainty and questionability. The uncertainty of individual observations is modeled in this book by fuzziness. Sequences of uncertain observations hence constitute fuzzy time series. By means of new discretization techniques for uncertain data it is now possible to correctly and completely retain data uncertainty in forecasting work. The book presents numerical methods which permit successful forecasting not only in engineering but also in many other ?elds such as environmental science or economics, assuming of course that a suitable sequence of observed data is available. By taking account of data uncertainty, the indiscriminate reduction of uncertain observations to real numbers is avoided. The larger information content described by uncertainty is retained, and compared with real data, provides a deeper insight into causal relationships. This in turn has practical consequences as far as the full?lment of technical requirements in engineering applications is concerned.
Additional text
From the reviews:
"The authors deal with a new and fascinating subject: forcasting the incertainty in civil engineering and environmental science. … the volume is a scientific monograph and represents a valuable contribution to the field. It is intended for civil engineers as well as to many professionals working in related fields." (Petre P. Teodorescu, Zentralblatt MATH, Vol. 1131 (9), 2008)
Report
From the reviews:
"The authors deal with a new and fascinating subject: forcasting the incertainty in civil engineering and environmental science. ... the volume is a scientific monograph and represents a valuable contribution to the field. It is intended for civil engineers as well as to many professionals working in related fields." (Petre P. Teodorescu, Zentralblatt MATH, Vol. 1131 (9), 2008)
