| Autor: |
Chauhan, Arsh, Chaudhary, Ritik |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3075 Issue 1, p1-7, 7p |
| Abstrakt: |
The cryptocurrency market is a rapidly changing environment that presents considerable problems for investors, traders, and analysts. Predicting cryptocurrency prices has been a popular topic in recent years. These currencies are used by millions of people worldwide, and their value fluctuate according to demand. Researchers and investors are making progress in constructing predicting models for cryptocurrency values, thanks to increased availability of historical data and breakthroughs in machine learning techniques. In this study we took four different models (Auto regressive integrated moving average, Support Vector Regression, Long Short Term Memory and Gated Recurrent Unit) to find the most effective way to predict price of cryptocurrency and then deploying into a web application for users to predict price. The study compares the mean absolute percentage error of these three models. For our study we have taken Litecoin a well-known cryptocurrency for model training and testing. The data has been fetched from yahoo finance which then goes through transformation and finally fitted into the ml model. The most effective model turns out to be LSTM with a MAPE of 37.45 to produce a prediction. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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