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.
Series
Edition
Softcover reprint of the original 1st ed. 2016
Language
Place of publication
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
14 farbige Abbildungen, 10 s/w Abbildungen
XXI, 158 p. 24 illus., 14 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 11 mm
Weight
ISBN-13
978-3-319-38726-0 (9783319387260)
DOI
10.1007/978-3-319-26293-2
Schweitzer Classification
PRITPAL SINGH, assistant professor in Central University of Rajasthan, India. He has an academic experience of more than 7 years.
He served as a Senior Postdoctoral Fellow in the Department of Electrical Engineering at the Taipei National University of Technology, Taiwan, from 2019-2020. He is working as an Adjunct Professor (Research) from November, 2020 in the Institute of Theoretical Physics, Jagiellonian University,
Poland. He is an active research member of Bio-Data Research Group (under TEAM-NET Program) in the Institute of Theoretical Physics, Jagiellonian University. His research interests include ambiguous set theory, soft computing, optimization algorithms (especially quantum-based optimization), time series forecasting, image analysis, fMRI data analysis, machine learning, mathematical modeling and simulation. He has published numerous papers in refereed SCI journals, conference proceedings, book chapters and book.
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.
