Summary of Review Of Deep Learning Models For Crypto Price Prediction: Implementation and Evaluation, by Jingyang Wu et al.
Review of deep learning models for crypto price prediction: implementation and evaluation
by Jingyang Wu, Xinyi Zhang, Fangyixuan Huang, Haochen Zhou, Rohtiash Chandra
First submitted to arxiv on: 19 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistical Finance (q-fin.ST); Machine Learning (stat.ML)
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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 reviews and evaluates various deep learning models for cryptocurrency price forecasting, including long short-term memory (LSTM) recurrent neural networks, convolutional neural networks (CNNs), and the Transformer model. The authors investigate univariate and multivariate approaches for multi-step ahead predicting of cryptocurrencies close-price, taking into account market volatility during the COVID-19 pandemic. They also explore the impact of training sets on prediction accuracy, using pre-COVID-19 data to forecast prices during the early pandemic period and COVID-19 data to predict prices from 2023 to 2024. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting how much cryptocurrency will be worth in the future. Right now, there are many different models that can do this job, but it’s not clear which ones work best because the value of cryptocurrency can change a lot. The authors looked at three types of deep learning models and found out which one worked the best for forecasting prices during the COVID-19 pandemic. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Transformer
