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Applications of soft computing in time series forecasting: simulation and modeling techniques

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 computin...

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Detalles Bibliográficos
Autor principal: Singh, Pritpal
Lenguaje:eng
Publicado: Springer 2016
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-26293-2
http://cds.cern.ch/record/2112798
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author Singh, Pritpal
author_facet Singh, Pritpal
author_sort Singh, Pritpal
collection CERN
description 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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institution Organización Europea para la Investigación Nuclear
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spelling cern-21127982021-04-21T20:01:07Zdoi:10.1007/978-3-319-26293-2http://cds.cern.ch/record/2112798engSingh, PritpalApplications of soft computing in time series forecasting: simulation and modeling techniquesEngineeringThis 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.  .Springeroai:cds.cern.ch:21127982016
spellingShingle Engineering
Singh, Pritpal
Applications of soft computing in time series forecasting: simulation and modeling techniques
title Applications of soft computing in time series forecasting: simulation and modeling techniques
title_full Applications of soft computing in time series forecasting: simulation and modeling techniques
title_fullStr Applications of soft computing in time series forecasting: simulation and modeling techniques
title_full_unstemmed Applications of soft computing in time series forecasting: simulation and modeling techniques
title_short Applications of soft computing in time series forecasting: simulation and modeling techniques
title_sort applications of soft computing in time series forecasting: simulation and modeling techniques
topic Engineering
url https://dx.doi.org/10.1007/978-3-319-26293-2
http://cds.cern.ch/record/2112798
work_keys_str_mv AT singhpritpal applicationsofsoftcomputingintimeseriesforecastingsimulationandmodelingtechniques