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Applications of Soft Computing in Time Series Forecasting : Simulation and Modeling Techniques / by Pritpal Singh

By: Singh, Pritpal.
Contributor(s): SpringerLink (Online service)
Material type: materialTypeLabelE-bookSeries: (Studies in Fuzziness and Soft Computing, 1434-9922; 330).Publisher: Cham : Springer International Publishing, 2016Edition: 1st ed.Description: 1 recurso en línea (XXI, 158 páginas) : 24 ilustraciones, 14 ilustraciones en color.ISBN: 9783319262932.Subject: Inteligencia artificial | Simulación por ordenadorDDC classification: 006.3 Online resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources Abstract: 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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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería Q342 .S564 2016 EB (Browse shelf(Opens below)) .i11588706 Acceso electrónico eBOOK .i11588706
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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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