Neural-Network-based Metamodeling for Financial Time Series Forecasting

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 crossvalidation 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.