Visible to the public Forecasting Crude Oil Prices Using Improved Deep Belief Network (IDBN) and Long-Term Short-Term Memory Network (LSTM)

TitleForecasting Crude Oil Prices Using Improved Deep Belief Network (IDBN) and Long-Term Short-Term Memory Network (LSTM)
Publication TypeConference Paper
Year of Publication2022
AuthorsHeravi, Mohammad Mahdi Lotfi, Khorrampanah, Mahsa, Houshmand, Monireh
Conference Name2022 30th International Conference on Electrical Engineering (ICEE)
Keywordsbelief networks, Crude oil price forecast, deep learning model, Economics, electrical engineering, energy resources, Fluctuations, Improved Deep Belief Network (IDBN), Long-Term Short Shor-Term Memory Network (LSTM), Metrics, Oils, Predictive models, Protocols, pubcrawl, Return Nervous Network (RNN)
AbstractHistorically, energy resources are of strategic importance for the social welfare and economic growth. So, predicting crude oil price fluctuations is an important issue. Since crude oil price changes are affected by many risk factors in markets, this price shows more complicated nonlinear behavior and creates more risk levels for investors than in the past. We propose a new method of prediction of crude oil price to model nonlinear dynamics. The results of the experiments show that the superior performance of the model based on the proposed method against statistical previous works is statistically significant. In general, we found that the combination of the IDBN or LSTM model lowered the MSE value to 4.65, which is 0.81 lower than the related work (Chen et al. protocol), indicating an improvement in prediction accuracy.
NotesISSN: 2642-9527
DOI10.1109/ICEE55646.2022.9827452
Citation Keyheravi_forecasting_2022