Predicting Impact of COVID-19 on Crude Oil Price Image with Directed Acyclic Graph Deep Convolution Neural Network
Abstract
Deep learning methods have achieved amazing results in sequential input, prediction and image classification. In this study, we propose image transformation of time series crude oil price by incorporating 2-D Directed Acyclic Graph to Convolutional Neural Network (DAG) based on image processing properties. Crude oil price time series is converted into 2-D images, utilizing 10 distinctive technical indicators. Geometric Brownian Motion was utilized to produces data for a 10-day time span. Thus, 10x10 sized 2-D images are constructed. Each image is then labelled as Buy or Sell depending on the returns of the time series. The results show that integrating DAG with CNN improves the prediction accuracy by 14.18%. DAG perform best with an accuracy of 99.16%, sensitivity of 100% and specificity of 99.19%. COVID-19 has negatively affected Nigeria crude oil price which indicates a downward trend of crude oil price. The study recommends poly-cultural economy of Nigeria economy for national development of the nation.
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https://www.offshore-technology.com/investment/covid-19-creates-supply-and-demand- crisis-for-oil-and-gas/ (Accessed on 14 May 2020)
https://www.worldometers.info/coronavirus/ (Accessed on 14 May 2020)
Nwoba, M. O, Nwonu, C. and Agbaeze, E.K, Impact of Fallen Oil Prices on the Nigeria Economy, Journal of Poverty, Investment and Development, 2017, vol. 33, pp. 75-82
Ayoola, J, “Does Volatility in Crude Oil Price Precipitate Macroeconomic Performance in Nigeria?” International Journal of Energy Economics Policy. 3 (2) 143-152,2013
Yusuf, M, “An Analysis of the Impact of Oil Price Shocks on the Growth of the Nigerian Economy”, 1970-2011. African Journal of Business Management, 2015, 9(3), 103-115.
LeCun Y, Bengio Y, Hinton G, Deep learning. Nature 521: 436–444, 2015
CiresAn, D., Meier, U., Masci, J., and Schmidhuber, J., “Multi-column deep neural network for traffic classification”. Neural Network, 2012, 32,333-338.
Jianfang Cao, Chenyan Wu, Lichao Chen, Hongyan Cui, and Guoqing Feng, “An Improved Convolutional Neural Network Algorithm and Its Application in Multilabel Image Labeling”, Computational Intelligence and Neuroscience 2019, Volume 1, pp. 1-11.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, Imagenet classification with deep convolutional neural networks,”in Advances in neural information processing systems, 2012, pp. 1097–1105.
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton, Speech recognition with deep recurrent neural networks, in Acoustics, speech and signal processing (icassp), ieee international conference on. IEEE, 2013, pp.6645–6649.
Yushi Chen, Hanlu Jiang, Chunyang Li, Xiuping Jia, and Pedram Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks”, IEEE Trans. Geosci. Remote Sens., 2016, vol. 54, no. 10, pp. 6232–6251.
S. Zhu, S. Shen, and X. Li, “Multimodal deep network learning-based image annotation”, Electronics Letters, 2015, vol. 51, no. 12, pp. 905-906.
J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and F. F. Li, “Imagenet: a large-scale hierarchical image database”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), 2009, pp. 248–250, Miami, FL, USA.
L. X. Jaing and J. Hou, Image annotation using the ensemble learning,Acta Autamatic Sinica, 2012, vol. 38, no. 8, pp. 1257–1262.
C.C. Aggarwal, Neural Networks and Deep Learning- A Textbook, Springer, 2018.
D. Ciresan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification”, 2012, DOI: 10.1109/CVPR.2012.6248110.
Simonyan, Karen, and Andrew Zisserman, “Very deep convolutional networks for large-scale image recognition”, 2014, arXiv preprint arXiv:1409-1556.
Kim, Ho-Joon, Joseph S. Lee, and Hyun-Seung Yang, Human action recognition using a modified convolutional neural network. International Symposium on Neural Networks. Springer Berlin Heidelberg, 2007.
Jonathan, A Weyn, Dale, R Durran, Rich Caruana, “Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks On a Cubed Sphere”, arXiv:2003.1927v1[physics.ao-ph] 15 Mar 2020, pp. 1-15.
W. Boomsma and J. Frellsen, “Spherical convolutions and their application in molecular modelling”. In Adv.Neural Inf. Process. Syst., 2017, volume 4, pages 3434–3444.
A. Chattopadhyay, E. Nabizadeh, and P. Hassanzadeh, “Analog forecasting of extreme-causing weather patterns using deep learning”. J. Adv. Model. Earth Syst., vol. 5, pp. 1-5.
G. R. Herman and R. S. Schumacher, “Money Doesn’t Grow on Trees, But Forecasts Do: Forecasting Extreme Precipitation with Random Forests”. Weather Rev., 2020, 146(5):1571–1600.
P. Golik, Z. Tuske, R. Schl ¨ uter, and H. Ney, “Convolutional neural networks for acoustic modeling of raw time signal in LVCSR”, in Proc. Annual Conference of International Speech Communication Association (INTERSPEECH), 2015.
P. Swietojanski, A. Ghoshal, and S. Renals, “Convolutional neural networks for distant speech recognition,” IEEE Signal Processing Letters, 2014, vol. 21, no. 9, pp. 1120–1124.
Anthimopoulos M, Christodoulidis S, Ebner L, et al. “Lung pattern classification for interstitial lung diseases using a deep convolutional neural network”. IEEE Trans Med Imaging. 2016, 35:1207–16.
Ker J, Wang L, Rao J, Lim T. “Deep learning applications in medical image analysis”. IEEE Access. 2018;6:9375–89.
Kenig, T., Kam, Z., Feuer, A. “Blind image deconvolution using machine learning for three dimensional microscopy”. IEEE Trans. Pattern Anal. Mach. Intell, 2010, 32(12), pp. 23-30.
S. Scher. “Toward Data-Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model with Deep Learning”. Geophys. Res. Lett., 2018, 45(22):12, 616-622.
David O. Oyewola, Aye Patrick Olabanji, Terrang.A.U, Jayeola Dare, “Deep Continuous-Time Models in Nigerian Stock Exchange Sector”, 2020, Journal of Science and Technology Research 2(1) pp. 99-113
David Oyewola, Danladi Hakimi, Kayode Adeboye, Musa Danjuma Shehu, “Using Five Machine Learning for Breast Cancer BiopsyPredictions Based on Mammographic Diagnosis”, International Journal of Engineering Technologies-IJET, 2016, Vol.2, No.4, pp. 142-14
DOI: https://doi.org/10.18196/jrc.2261
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