Applications of Soft Computing in Time Series Forecasting

Pritpal Singh

ISBN 10: 331938726X / ISBN 13: 9783319387260

Published by Springer International Publishing, Springer International Publishing Aug 2016, 2016

About this Item

Neuware -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 seriesmodeling, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and government organizations.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 180 pp. Englisch.

Seller Inventory # 9783319387260

Bibliographic Details

• Title: Applications of Soft Computing in Time Series Forecasting
• Publisher: Springer International Publishing, Springer International Publishing Aug 2016
• Publication Date: 2016
• Language: English
• ISBN 10: 331938726X
• ISBN 13: 9783319387260
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.