| Title | Forecasting Crude Oil Prices Using Improved Deep Belief Network (IDBN) and Long-Term Short-Term Memory Network (LSTM) |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Heravi, Mohammad Mahdi Lotfi, Khorrampanah, Mahsa, Houshmand, Monireh |
| Conference Name | 2022 30th International Conference on Electrical Engineering (ICEE) |
| Keywords | belief 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) |
| Abstract | Historically, 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. |
| Notes | ISSN: 2642-9527 |
| DOI | 10.1109/ICEE55646.2022.9827452 |
| Citation Key | heravi_forecasting_2022 |
Forecasting Crude Oil Prices Using Improved Deep Belief Network (IDBN) and Long-Term Short-Term Memory Network (LSTM)