Neural Networks for Financial Forecasting
Neural Networks for Financial Forecasting
Inductive Learning Algorithms for Complex Systems Modeling
Inductive Learning Algorithms for Complex Systems Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks
Journal of Management Information Systems
A local boosting algorithm for solving classification problems
Computational Statistics & Data Analysis
An efficient modified boosting method for solving classification problems
Journal of Computational and Applied Mathematics
Optimal cooperation between external criterion and data division in GMDH
International Journal of Systems Science
Regularized least squares fuzzy support vector regression for financial time series forecasting
Expert Systems with Applications: An International Journal
A neural-network-based nonlinear metamodeling approach to financial time series forecasting
Applied Soft Computing
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Hybrid GMDH-type modeling for nonlinear systems: Synergism to intelligent identification
Advances in Engineering Software
Structure identification of Bayesian classifiers based on GMDH
Knowledge-Based Systems
| Hi-index | 12.05 |
Traditional GMDH (group method of data handling) method has been applied in series forecasting successfully many times. In this paper, we bring concept of diversity into GMDH to improve the noise-immunity ability. Five diversity metrics are used as external criteria to construct a new kind of GMDH forecasting models called D-GMDH. To assess the effectiveness of D-GMDH, we compare them with traditional GMDH method, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN), and find out that the two models - D-GMDH (chi) and D-GMDH (cor) - are better than the others among the five D-GMDH models. The two better models are then used to forecast financial time series with noise. Results show that the two new proposed models can provide high forecasting accuracy in noisy environment.