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Neural Networks for TimeSeries Forecasting
 G. Peter Zhang
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Abstract
Neural networks has become an important method for time series forecasting. There is increasing interest in using neural networks to model and forecast time series. This chapter provides a review of some recent developments in time series forecasting with neural networks, a brief description of neural networks, their advantages over traditional forecasting models, and some recent applications. Several important data and modeling issues for time series forecasting are highlighted. In addition, recent developments in several methodological areas such as seasonal time series modeling, multiperiod forecasting, and the ensemble method are reviewed.
 Title
 Neural Networks for TimeSeries Forecasting
 Reference Work Title
 Handbook of Natural Computing
 Pages
 pp 461477
 Copyright
 2012
 DOI
 10.1007/9783540929109_14
 Print ISBN
 9783540929093
 Online ISBN
 9783540929109
 Publisher
 Springer Berlin Heidelberg
 Copyright Holder
 SpringerVerlag Berlin Heidelberg
 Additional Links
 Topics
 Industry Sectors
 eBook Packages
 Editors

 Grzegorz Rozenberg ^{(251)} ^{(252)}
 Thomas Bäck ^{(253)}
 Joost N. Kok ^{(254)}
 Editor Affiliations

 251. LIACS, Leiden University
 252. Computer Science Department, University of Colorado
 253. LIACS, Leiden University
 254. LIACS, Leiden University
 Authors

 G. Peter Zhang ^{(00141)}
 Author Affiliations

 00141. Department of Managerial Sciences, Georgia State University, Atlanta, GA, USA
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