Observations of uncertainty in measured data with time improves forecasting capability in a wide range of fields in engineering. This book provides an introduction to uncertainty forecasting based on fuzzy time series. It details descriptive, modeling, and forecasting methods for fuzzy time series. Coverage places emphasis on forecasting based on fuzzy random processes as well as forecasting involving fuzzy neuronal networks.
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.
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.
Univ.-Prof. Dr.-Ing. habil. Bernd Möller (*1941) studied civil engineering. From 1994 –1996 he was professor for Numerical Methods and Computer Science in Engineering at the University of Braunschweig, and from 1996 – 2006 professor for Solid and Structural Mechanics at the University of Technology in Dresden, Germany. Professor Möller has been retired since 2006.
Dr.-Ing. Uwe Reuter (*1979) studied civil engineering. From 2003 – 2007 he was research associate at the Institute of Statics and Dynamics of Structures at the University of Technology in Dresden, Germany. Today he is head of the Computing Center at the Faculty of Civil Engineering at the University of Technology in Dresden.
Fuzzy time series can be applied in many fields in engineering like environmental engineering or civil engineering
Two simulation-based important forecasting strategies are explained: forecasting based on fuzzy-ARMA-processes or fuzzy-white-noise-processes and forecasting based on fuzzy artificial neural networks
A complete new description of uncertain data as incremental fuzzy data is given
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