Summary of Multi-source Hard and Soft Information Fusion Approach For Accurate Cryptocurrency Price Movement Prediction, by Saeed Mohammadi Dashtaki et al.
Multi-Source Hard and Soft Information Fusion Approach for Accurate Cryptocurrency Price Movement Prediction
by Saeed Mohammadi Dashtaki, Mehdi Hosseini Chagahi, Behzad Moshiri, Md. Jalil Piran
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach called Hard and Soft Information Fusion (HSIF) for accurately predicting cryptocurrency price trends. By combining historical price records, technical indicators, and social media data from X (formerly Twitter), the model improves forecast accuracy. The HSIF approach uses BERT-based sentiment analysis to extract insights from news headlines and tweets about cryptocurrencies. The BiLSTM model is employed to process information in both forward and backward directions, capturing long-term dependencies. Empirical findings show that the HSIF approach outperforms single-source data models on the Bitcoin dataset, achieving an accuracy of 96.8%. By incorporating social sentiment analysis, the model supplements technical analysis-based predictions. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict how much cryptocurrency prices will change in the future. It uses a new way to combine different types of information: past price records, special indicators that show trends, and what people are saying on Twitter about cryptocurrencies. This combination of information helps make better predictions than just using one type of data. The researchers tested their method on Bitcoin data and found it was very accurate – 96.8%! They also learned how social media can affect cryptocurrency prices. |
Keywords
» Artificial intelligence » Bert