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A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting

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  • Urolagin, Siddhaling
  • Sharma, Nikhil
  • Datta, Tapan Kumar

Abstract

Oil price plays a vital role in a country's economy. Oil price forecasting helps in making better economic planning and decisions. The fluctuation in the oil price occurs due to several factors that make oil price forecasting a challenging task. Deep learning models such as Recurrent Neural Networks (RNN) have emerged as a successful method in solving many nonlinear and complex problems. In this research, a combined architecture of Multivariate Long Short Term Memory (MLSTM) is proposed with Mahalanobis and Z-score transformations. These transformations improve the data to uncorrelated and standardized variance, thus making data more suitable for regression analysis. The available historical time-series data on the West Texas Intermediate (WTI) oil prices and the factors affecting the oil prices are collected to form the data set. The feature selection process using selectKBest and correlation analysis is carried out. We develop six variants of MLSTM models with different features, transformations, and outlier elimination. The MLSTM model with Z-score transformation on the selected features and outlier eliminated data gives an RMSE of 0.212 and an R2 score of 0.954. This indicates that our model performed well.

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  • Urolagin, Siddhaling & Sharma, Nikhil & Datta, Tapan Kumar, 2021. "A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting," Energy, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:energy:v:231:y:2021:i:c:s0360544221012111
    DOI: 10.1016/j.energy.2021.120963
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    1. Xiaodan Liang & Zhaodi Ge & Liling Sun & Maowei He & Hanning Chen, 2019. "LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, July.
    2. Li, Xuerong & Shang, Wei & Wang, Shouyang, 2019. "Text-based crude oil price forecasting: A deep learning approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1548-1560.
    3. Jia Liao & Yu Shi & Xiangyun Xu, 2018. "Why Is the Correlation between Crude Oil Prices and the US Dollar Exchange Rate Time-Varying?—Explanations Based on the Role of Key Mediators," IJFS, MDPI, vol. 6(3), pages 1-13, June.
    4. Andreas Breitenfellner & Jesus Crespo Cuaresma, 2008. "Crude Oil Prices and the USD/EUR Exchange Rate," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 4.
    5. Xuluo Yin & Jiangang Peng & Tian Tang, 2018. "Improving the Forecasting Accuracy of Crude Oil Prices," Sustainability, MDPI, vol. 10(2), pages 1-9, February.
    6. Wu, Yu-Xi & Wu, Qing-Biao & Zhu, Jia-Qi, 2019. "Improved EEMD-based crude oil price forecasting using LSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 114-124.
    7. Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.
    8. Alexopoulos, Thomas A., 2017. "The growing importance of natural gas as a predictor for retail electricity prices in US," Energy, Elsevier, vol. 137(C), pages 219-233.
    9. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
    10. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    11. Lee, Bi-Juan & Yang, Chin Wei & Huang, Bwo-Nung, 2012. "Oil price movements and stock markets revisited: A case of sector stock price indexes in the G-7 countries," Energy Economics, Elsevier, vol. 34(5), pages 1284-1300.
    12. Hamilton, James D, 1983. "Oil and the Macroeconomy since World War II," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 228-248, April.
    13. Zhang, Yue-Jun & Wei, Yi-Ming, 2010. "The crude oil market and the gold market: Evidence for cointegration, causality and price discovery," Resources Policy, Elsevier, vol. 35(3), pages 168-177, September.
    14. Zhaojie Luo & Xiaojing Cai & Katsuyuki Tanaka & Tetsuya Takiguchi & Takuji Kinkyo & Shigeyuki Hamori, 2019. "Can We Forecast Daily Oil Futures Prices? Experimental Evidence from Convolutional Neural Networks," JRFM, MDPI, vol. 12(1), pages 1-13, January.
    Full references (including those not matched with items on IDEAS)

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