This book reports on an in-depth study of fuzzy time series (FTS) modeling. It reviews and summarizes previous research work in FTS modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and evolutionary computing (EC), focusing on how these techniques can be integrated into different phases of the FTS modeling approach. In particular, the book describes novel methods resulting from the hybridization of FTS modeling approaches with neural networks and particle swarm optimization. It also demonstrates how a new ANN-based model can be successfully applied in the context of predicting Indian summer monsoon rainfall. Thanks to its easy-to-read style and the clear explanations of the models, the book can be used as a concise yet comprehensive reference guide to fuzzy time series modeling, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and government organizations.

Provides the readers with the necessary theoretical background and practical tools for designing time series forecasting models using a combination of soft computing techniques Presents improved methods for fuzzy time series modeling Includes a detailed analysis of the reported models, from their formulation, to the empirical tests, including their performance measures Shows a model implementation for summer monsoon rainfall prediction Includes supplementary material: sn.pub/extras

Inhalt
Introduction.- Fuzzy Time Series Modeling Approaches: A Review.- Efficient One-Factor Fuzzy Time Series Forecasting Model.- High-order Fuzzy-Neuro Time Series Forecasting Model.- Two-Factors High-order Neuro-Fuzzy Forecasting Model.- FTS-PSO Based Model for M-Factors Time Series Forecasting.- Indian Summer Monsoon Rainfall Prediction.- Conclusions.