Summary of Enhancing Cryptocurrency Market Forecasting: Advanced Machine Learning Techniques and Industrial Engineering Contributions, by Jannatun Nayeem Pinky and Ramya Akula
Enhancing Cryptocurrency Market Forecasting: Advanced Machine Learning Techniques and Industrial Engineering Contributions
by Jannatun Nayeem Pinky, Ramya Akula
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper comprehensively reviews machine learning techniques applied to cryptocurrency price prediction from 2014 to 2024. It explores various ML algorithms, including linear models, tree-based approaches, and advanced deep learning architectures such as transformers and large language models. The authors also examine the role of sentiment analysis in capturing market sentiment from textual data like social media posts and news articles to anticipate price fluctuations. Additionally, they highlight the significant role of industrial engineers in refining predictive models by applying principles of process optimization, efficiency, and risk mitigation to improve computational performance and data management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machines can be used to predict the price of cryptocurrencies like Bitcoin. It talks about different types of computer programs that try to guess what will happen to prices based on past information. The authors also look at how people’s feelings and opinions (sentiment) can affect market prices, by looking at things like social media posts and news articles. They think industrial engineers, who are experts in making sure systems work well, can help make these predictions more accurate. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Optimization
