Cascade networks model to predict the crude oil prices in Iraq

Suhair A. Al-Hilfi, Maysaa Abd Ulkareem Naser


Oil prices are inherently volatile, and they used to suffer from many fluctuations and changes. Therefore, oil prices prediction is the subject of many studies in the field, some researchers concentrated on the key factors that could influence the prediction accuracy, while the others focused on designing models that forecast the prices with high accuracy. To help the institutions and companies to hedge against any sudden changes and develop right decisions that support the global economy, in this project the concept of cascade networks model to predict the crude oil prices has been adopted, that can be considered relatively as new initiative in the field. The model is used to predict the Iraqi oil prices since as its commonly known that the economy in Iraq is totally depend on oil. Therefore, it is vital to develop a better perception about the crude oil price dynamics because its volatility can cause a sudden economic crisis.


Cascade neural networks; Convolutional LSTM; Crude oil price in Iraq; Deep learning

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578