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Fuzzy time series can be applied in many fields in engineering, such as environmental engineering or civil engineering
Explains two important, simulation-based forecasting strategies: fuzzy-white-noise-processes and fuzzy artificial neural networks
Offers a complete new description of uncertain data as incremental fuzzy data
This book deals with uncertainty forecasting based on a fuzzy time series approach, including fuzzy random processes and artificial neural networks. A consideration of data and measurement uncertainty enhances forecasting in a wide range of applications, particularly in the fields of engineering, environmental science and civil engineering.
Uncertain data are described by means of a new incremental fuzzy representation which permits a complete and accurate estimation of uncertainty.
The book is aimed at engineers as well as professionals working in related fields. Descriptive, modeling and forecasting methods pertaining to fuzzy time series are introduced and explained in detail. Emphasis is placed on forecasting with the aid of fuzzy random processes, such as fuzzy ARMA processes and fuzzy white-noise processes, as well as forecasting based on artificial neural networks.
All numerical algorithms are comprehensively described and demonstrated by way of practical examples.
Content Level »Research
Keywords »Forecasting - Fuzzy Data - Fuzzy artificial neutral networks - Measure - Random Values - Time series - Uncertainty - artificial neural network - fuzzy