Name
Title | ||
|---|---|---|
A Novel Hybrid Ensemble Model To Predict Ftse100 Index By Combining Neural Network And Eemd |
Abstract | ||
|---|---|---|
Prediction stock price is considered the most challenging and important financial topic. Thus, its complexity, nonlinearity and much other characteristic, single method could not optimize a good result. Hence, this paper proposes a hybrid ensemble model based on BP neural network and EEMD to predict FTSE100 closing price. In this paper there are five hybrid prediction models, EEMD-NN, EEMD-Bagging-NN, EEMD-Cross validation-NN, EEMD-CV-Bagging-NN and EEMD-NN-Proposed method. Experimental result shows that EEMD-Bagging-NN, EEMD-Cross validation-NN and EEMID-CV-Bagging-NN models performance are a notch above EEMD-NN and significantly higher than the single-NN model. In addition, EEMD-NN-Proposed method prediction performance superiority is demonstrated comparing with the all presented model in this paper, and was feasible and effective in prediction FTSE100 closing price. As a result of the significant performance of the proposed method, the method can be utilized to predict other financial time series data. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 EUROPEAN CONTROL CONFERENCE (ECC) | white noise,artificial neural networks,mathematical model,time series analysis,predictive models,accuracy |
Field | DocType | Citations |
Time series,Data mining,Stock price,Nonlinear system,Ensemble forecasting,White noise,Artificial intelligence,Predictive modelling,Engineering,Artificial neural network,Machine learning | Conference | 3 |
PageRank | References | Authors |
0.42 | 15 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| bashar alhnaity | 1 | 5 | 1.12 |
| Maysam Abbod | 2 | 38 | 7.15 |