Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model

Siti Norbaiti binti Abdullah · Xiao‐Jun Zeng

64 citations0/0 files storedInternational Joint Conference on Neural Networks (IJCNN)

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Stock Market Forecasting Methods Forecasting Techniques and Applications Market Dynamics and Volatility

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.

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References

2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm 1994Neural Network Time Series: Forecasting of Financial Markets 1993Feedforward Neural Nets as Models for Time Series Forecasting 1995Neural network time series forecasting of financial markets 2006A New Method for Crude Oil Price Forecasting Based on Support Vector Machines 2001A semiparametric approach to short-term oil price forecasting 2005A monthly crude oil spot price forecasting model using relative inventories 2000Are oil markets chaotic? A non-linear dynamic analysis

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