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

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Singh P. Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques
Springer, 2016. — 166 p. —( Studies in Fuzziness and Soft Computing). — ISBN: 9783319262925, 9783319262932
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.
Contents:
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
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