Applications of Soft Computing in Time Series Forecasting

Simulation and Modeling Techniques

Nonfiction, Science & Nature, Science, Physics, Chaotic Behavior, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Applications of Soft Computing in Time Series Forecasting by Pritpal Singh, Springer International Publishing
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Author: Pritpal Singh ISBN: 9783319262932
Publisher: Springer International Publishing Publication: November 22, 2015
Imprint: Springer Language: English
Author: Pritpal Singh
ISBN: 9783319262932
Publisher: Springer International Publishing
Publication: November 22, 2015
Imprint: Springer
Language: English

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.

 

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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