Summary of Transformer-based Approach For Ethereum Price Prediction Using Crosscurrency Correlation and Sentiment Analysis, by Shubham Singh et al.
Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis
by Shubham Singh, Mayur Bhat
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Pricing of Securities (q-fin.PR)
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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 research introduces a transformer-based neural network for Ethereum cryptocurrency price forecasting, leveraging its capabilities to analyze sentiment and correlated prices. By combining various features, including volume, sentiment, and correlated cryptocurrency prices, the model outperforms traditional ANN and MLP models on certain parameters. The findings suggest that sentiments play a significant role in driving cryptocurrency price movements, potentially creating an illusion of causality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses a special kind of computer program called a transformer to predict Ethereum’s price. It looks at lots of different things that might affect the price, like what people are saying about it and how much other cryptocurrencies are selling for. The results show that this approach works better than some other ways of predicting prices. This is important because understanding why cryptocurrency prices change can help us make better decisions. |
Keywords
* Artificial intelligence * Neural network * Transformer
