Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques

✍ Scribed by Pritpal Singh (auth.)

• Publisher: Springer International Publishing
• Year: 2016
• Tongue: English
• Leaves: 166
• Series: Studies in Fuzziness and Soft Computing 330
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xxi
Introduction....Pages 1-9
Fuzzy Time Series Modeling Approaches: A Review....Pages 11-39
Efficient One-Factor Fuzzy Time Series Forecasting Model....Pages 41-62
High-Order Fuzzy-Neuro Time Series Forecasting Model....Pages 65-81
Two-Factors High-Order Neuro-Fuzzy Forecasting Model....Pages 83-97
FTS-PSO Based Model for M-Factors Time Series Forecasting....Pages 99-126
Indian Summer Monsoon Rainfall Prediction....Pages 127-148
Conclusions....Pages 149-153
Back Matter....Pages 155-158

✦ Subjects

Computational Intelligence; Nonlinear Dynamics; Simulation and Modeling; Artificial Intelligence (incl. Robotics)