A neural-network-based nonlinear metamodeling approach to financial time series forecasting
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 563-574 |
Journal / Publication | Applied Soft Computing Journal |
Volume | 9 |
Issue number | 2 |
Publication status | Published - Mar 2009 |
Link(s)
Abstract
In financial time series forecasting, the problem that we often encounter is how to increase the prediction accuracy as possible using the financial data with noise. In this study, we discuss the use of supervised neural networks as a meta-learning technique to design a financial time series forecasting system to solve this problem. In this system, some data sampling techniques are first used to generate different training subsets from the original datasets. In terms of these different training subsets, different neural networks with different initial conditions or training algorithms are then trained to formulate different prediction models, i.e., base models. Subsequently, to improve the efficiency of predictions of metamodeling, the principal component analysis (PCA) technique is used as a pruning tool to generate an optimal set of base models. Finally, a neural-network-based nonlinear metamodel can be produced by learning from the selected base models, so as to improve the prediction accuracy. For illustration and verification purposes, the proposed metamodel is conducted on four typical financial time series. Empirical results obtained reveal that the proposed neural-network-based nonlinear metamodeling technique is a very promising approach to financial time series forecasting. © 2008 Elsevier B.V. All rights reserved.
Research Area(s)
- Artificial neural networks, Data sampling, Financial time series forecasting, Meta-learning, Metamodeling, PCA
Citation Format(s)
A neural-network-based nonlinear metamodeling approach to financial time series forecasting. / Yu, Lean; Wang, Shouyang; Lai, Kin Keung.
In: Applied Soft Computing Journal, Vol. 9, No. 2, 03.2009, p. 563-574.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review