Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques von Singh, Pritpal: Brand New Paperback (2016)

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reprint edition. 158 pages. 9.25x6.10x0.38 inches. In Stock.

Bestandsnummer des Verk�ufers x-331938726X

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Bibliografische Details

Titel: Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques
Verlag: Springer-Verlag New York Inc
Ver�ffentlichungsdatum: 2016
Sprache: Englisch
ISBN-10: 331938726X
ISBN-13: 9783319387260
Bindung: Paperback
Zustand: Brand New

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Inhaltsangabe

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.

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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.

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