Summary of Prediction Of Brent Crude Oil Price Based on Lstm Model Under the Background Of Low-carbon Transition, by Yuwen Zhao et al.
Prediction of Brent crude oil price based on LSTM model under the background of low-carbon transition
by Yuwen Zhao, Baojun Hu, Sizhe Wang
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed deep learning model utilizes three layers of Long Short-Term Memory (LSTM) units to predict the European Brent crude oil spot price over a short-term horizon. The study leverages real-world data from the US Energy Information Administration and demonstrates the LSTM model’s ability to capture overall price trends, despite some deviation during periods of sharp price fluctuations. This research not only verifies the applicability of LSTMs in energy market forecasting but also provides valuable insights for policymakers and investors navigating the uncertainty surrounding crude oil prices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses a special kind of artificial intelligence called deep learning to predict the price of oil. The researchers used real data from the US Energy Information Administration and found that this type of AI model can accurately forecast oil prices most of the time, even when there are big changes in the market. This is important for people who make decisions about energy policy and investments. |
Keywords
» Artificial intelligence » Deep learning » Lstm
