Neural-Network-based Metamodeling for Financial Time Series Forecasting

DOI

10.2991/jcis.2006.172How to use a DOI?

Keywords

Metamodeling, neural network, financial time series forecasting, cross-validation

Abstract

In the financial time series forecasting field, the problem that we often encountered is how to increase the predict accuracy as possible using the noisy financial data. In this study, we discuss the use of supervised neural networks as the metamodeling technique to design a financial time series forecasting system to solve this problem. First of all, a cross-validation technique is used to generate different training subsets. Based on the different training subsets, the different neural predictors with different initial conditions or training algorithms is then trained to formulate different forecasting models, i.e., base models. Finally, a neural-network-based metamodel can be produced by learning from all base models so as to improve the model accuracy. For verification, two real-world financial time series is used for testing.

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Kin Keung Lai
AU  - Lean YU
AU  - Shouyang Wang
AU  - Chengxiong Zhou
PY  - 2006/10
DA  - 2006/10
TI  - Neural-Network-based Metamodeling for Financial Time Series Forecasting
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SP  - 609
EP  - 612
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.172
DO  - 10.2991/jcis.2006.172
ID  - Lai2006/10
ER  -