A dynamic meta-learning rate-based model for gold market forecasting

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

21 Scopus Citations
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Author(s)

  • Shifei Zhou
  • Kin Keung Lai
  • Jerome Yen

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)6168-6173
Journal / PublicationExpert Systems with Applications
Volume39
Issue number6
Publication statusPublished - May 2012

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. © 2011 Elsevier Ltd. All rights reserved.

Research Area(s)

  • BPNN, EMD, Forecasting, Meta-learning

Citation Format(s)

A dynamic meta-learning rate-based model for gold market forecasting. / Zhou, Shifei; Lai, Kin Keung; Yen, Jerome.

In: Expert Systems with Applications, Vol. 39, No. 6, 05.2012, p. 6168-6173.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review