Summary of Leveraging Time Series Categorization and Temporal Fusion Transformers to Improve Cryptocurrency Price Forecasting, by Arash Peik et al.
Leveraging Time Series Categorization and Temporal Fusion Transformers to Improve Cryptocurrency Price Forecasting
by Arash Peik, Mohammad Ali Zare Chahooki, Amin Milani Fard, Mehdi Agha Sarram
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Statistical Finance (q-fin.ST)
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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 method categorizes financial time series into similar subseries and uses deep learning models with attention mechanisms to predict the price of cryptocurrencies. The approach aims to improve prediction accuracy by leveraging the similarity in behavior between subseries. To address the challenge of limited training data, the researchers propose combining time series data from other cryptocurrencies to increase the amount of data for each category. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to predict cryptocurrency prices by grouping similar market trends together and using deep learning models to make predictions. The method tries to solve the problem of not having enough data to train these complex models, which can lead to poor performance. By combining data from different cryptocurrencies, the researchers hope to improve the accuracy of their models. |
Keywords
» Artificial intelligence » Attention » Deep learning » Time series
