Neural-Network-based Metamodeling for Financial Time Series Forecasting
- Kin Keung Lai 0, Lean YU, Shouyang Wang, Chengxiong Zhou
- Corresponding Author
- Kin Keung Lai
0City University of Hong Kong
Available Online undefined NaN.
- https://doi.org/10.2991/jcis.2006.172How to use a DOI?
- Metamodeling, neural network, financial time series forecasting, cross-validation
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Kin Keung Lai AU - Lean YU AU - Shouyang Wang AU - Chengxiong Zhou PY - NaN/NaN DA - NaN/NaN TI - Neural-Network-based Metamodeling for Financial Time Series Forecasting BT - 9th Joint International Conference on Information Sciences (JCIS-06) PB - Atlantis Press UR - https://doi.org/10.2991/jcis.2006.172 DO - https://doi.org/10.2991/jcis.2006.172 ID - LaiNaN/NaN ER -