Forecasting Crude Oil Prices: a Deep Learning based Model

Yanhui Chen · Kaijian He · Kwok Fai Tso

127 citations0/0 files storedProcedia Computer Science

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

Abstract

With the popularity of the deep learning model in the engineering fields, it has attracted significant research interests in the economic and finance fields. In this paper, we use the deep learning model to capture the unknown complex nonlinear characteristics of the crude oil price movement. We further propose a new hybrid crude oil price forecasting model based on the deep learning model. Using the proposed model, major crude oil price movement is analyzed and modeled. The performance of the proposed model is evaluated using the price data in the WTI crude oil markets. The empirical results show that the proposed model achieves the improved forecasting accuracy.

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References

2016Deep Learning for Finance: Deep Portfolios 2015A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units 2015Forecasting exchange rate using deep belief networks and conjugate gradient method 2015Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction 2016Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations

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