Summary of Comparison Of Different Artificial Neural Networks For Bitcoin Price Forecasting, by Silas Baumann et al.
Comparison of different Artificial Neural Networks for Bitcoin price forecasting
by Silas Baumann, Karl A. Busch, Hamza A. A. Gardi
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 study explores the effect of varying sequence lengths on predicting cryptocurrency returns using Artificial Neural Networks (ANNs). By employing Mean Absolute Error (MAE) as a threshold criterion, researchers aim to improve prediction accuracy by excluding minor returns. The evaluation focuses on predicted returns exceeding this threshold. The study compares four sequence lengths (168 hours, 72 hours, 24 hours, and 12 hours) with a return prediction interval of 2 hours. Findings show the impact of sequence length on prediction accuracy, highlighting the potential for optimized configurations in financial forecasting models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well Artificial Neural Networks (ANNs) can predict future changes in cryptocurrency values based on different time lengths. The team wants to make these predictions more accurate by ignoring small changes and focusing on bigger ones. They tested four different time lengths (one week, three days, one day, and 12 hours) to see which one works best for predicting big changes. The results show that the length of time used affects how well ANNs predict cryptocurrency values, which could be useful for creating better financial forecasting models. |
Keywords
» Artificial intelligence » Mae
