Summary of Prediction Of Cryptocurrency Prices Using Lstm, Svm and Polynomial Regression, by Novan Fauzi Al Giffary et al.
Prediction Of Cryptocurrency Prices Using LSTM, SVM And Polynomial Regression
by Novan Fauzi Al Giffary, Feri Sulianta
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A paper presents a performance comparison of three forecasting algorithms (Long Short-Term Memory, Support Vector Machine, and Polynomial Regression) for predicting cryptocurrency prices. The algorithms are evaluated based on the mean square error benchmark. Results show that the Support Vector Machine with a linear kernel produces the smallest mean square error value of 0.02, making it the most suitable model for crypto currency price forecasting. This work aims to address the uncertainty in crypto coin values and provide investors with a reliable method for predicting future prices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of researchers tried to figure out which algorithm is best at predicting the value of cryptocurrencies like Bitcoin. They tested three different models: Long Short-Term Memory, Support Vector Machine, and Polynomial Regression. The results showed that one of these algorithms, called Support Vector Machine with a linear kernel, did the best job of guessing future prices correctly. This research could help people who invest in cryptocurrencies make better decisions about when to buy or sell. |
Keywords
* Artificial intelligence * Regression * Support vector machine
