Summary of Cryptocurrency Price Forecasting Using Xgboost Regressor and Technical Indicators, by Abdelatif Hafid et al.
Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators
by Abdelatif Hafid, Maad Ebrahim, Ali Alfatemi, Mohamed Rahouti, Diogo Oliveira
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 machine learning approach aims to predict cryptocurrency prices by leveraging technical indicators such as EMA and MACD to train an XGBoost regressor model. The study focuses on analyzing the closing prices of Bitcoin and demonstrates promising results through various simulations, suggesting its potential in aiding traders and investors in dynamic market conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to predict cryptocurrency prices by using special tools like moving averages (EMA) and divergence (MACD). It shows that we can use machine learning to make predictions about Bitcoin prices. This is important because it could help people who trade or invest in cryptocurrencies make better decisions. |
Keywords
» Artificial intelligence » Machine learning » Xgboost
