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A Dynamic Meta-Learning Rate-Based Model for Gold Market Forecasting
Zhou, Shifei1; Lai, Kin Keung1; Yen, Jerome2
2013
Source PublicationEXPERT SYSTEMS WITH APPLICATIONS
ISSN0957-4174
Volume39Issue:6Pages:6168-6173
Abstract

In this paper, an improved EMD meta-learning rate-based model for gold price forecasting is proposed. First, we adopt the EMD method to divide the time series data into different subsets. Second, a back-propagation neural network model (BPNN) is used to function as the prediction model in our system. We update the online learning rate of BPNN instantly as well as the weight matrix. Finally, a rating method is used to identify the most suitable BPNN model for further prediction. The experiment results show that our system has a good forecasting performance.

KeywordEmd Forecastin Meta-learning Bpnn
DOI10.1016/j.eswa.2011.11.115
Indexed BySSCI
Language英语
WOS Research AreaOperations Research & Management Science ; Computer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS IDWOS:000301013700006
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Citation statistics
Cited Times [WOS]:20   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Business Administration
DEPARTMENT OF ACCOUNTING AND INFORMATION MANAGEMENT
Affiliation1.Department of Management Sciences, College of Business, City University of Hong Kong
2.Department of Finance, Hong Kong University of Science and Technology, Kowloon, Hong Kong
Recommended Citation
GB/T 7714
Zhou, Shifei,Lai, Kin Keung,Yen, Jerome. A Dynamic Meta-Learning Rate-Based Model for Gold Market Forecasting[J]. EXPERT SYSTEMS WITH APPLICATIONS,2013,39(6):6168-6173.
APA Zhou, Shifei,Lai, Kin Keung,&Yen, Jerome.(2013).A Dynamic Meta-Learning Rate-Based Model for Gold Market Forecasting.EXPERT SYSTEMS WITH APPLICATIONS,39(6),6168-6173.
MLA Zhou, Shifei,et al."A Dynamic Meta-Learning Rate-Based Model for Gold Market Forecasting".EXPERT SYSTEMS WITH APPLICATIONS 39.6(2013):6168-6173.
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