Predicting Impact of COVID-19 on Crude Oil Price Image with Directed Acyclic Graph Deep Convolution Neural Network

David O. Oyewola; Akomolafe Femi Augustine; Emmanuel Gbenga Dada; Asabe Ibrahim

doi:10.18196/jrc.2261

Authors

David O. Oyewola Federal University of Kashere, Gombe http://orcid.org/0000-0001-9638-8764
Akomolafe Femi Augustine Federal University of Kashere, Gombe State.
Emmanuel Gbenga Dada University of Maiduguri
Asabe Ibrahim Federal University of Kashere

DOI:

https://doi.org/10.18196/jrc.2261

Keywords:

COVID-19, Recurrent Neural Network, Convolution Neural Network, Directed Acyclic Graph, Long Short Term Memory, Geometric Brownian Motion

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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