Name
Title | ||
|---|---|---|
Machine Learning Approach For Crude Oil Price Prediction With Artificial Neural Networks-Quantitative (Ann-Q) Model |
Abstract | ||
|---|---|---|
The volatility of crude oil market and its chain effects to the world economy augmented the interest and fear of individuals, public and private sectors. Previous statistical and econometric techniques used for prediction, offer good results when dealing with linear data. Nevertheless, crude oil price series deal with high nonlinearity and irregular events. The continuous usage of statistical and econometric techniques for crude oil price prediction might demonstrate demotions to the prediction performance. Machine Learning and Computational Intelligence approach through combination of historical quantitative data with qualitative data from experts' view and news is a remedy proposed to predict this. This paper will discuss the first part of the research, focusing on to (i) the development of Hierarchical Conceptual (HC) model and (ii) the development of Artificial Neural Networks-Quantitative (ANN-Q) model. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596602 | 2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 |
Keywords | Field | DocType |
data models,statistical analysis,econometrics,pricing,computational intelligence,private sector,artificial neural networks,neural nets,predictive models,artificial neural network,qualitative data,feature extraction,learning artificial intelligence,machine learning,petroleum,computational modeling | Data modeling,Computational intelligence,Qualitative property,Computer science,Crude oil,Feature extraction,Artificial intelligence,Petroleum,Artificial neural network,Volatility (finance),Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 6 | 0.73 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siti Norbaiti Abdullah | 1 | 6 | 0.73 |
| Xiaoqin Zeng | 2 | 407 | 32.97 |