Bitcoin Price Prediction Using Machine Learning Techniques
DOI:
https://doi.org/10.54060/JIEEE/003.01.005Keywords:
Bitcoin, Prediction, Time-Series, Predictive Model, Deep Learning, Long-Short Term Memory, Recurrent Neural Network, Real-time Data, Mean Absolute Error, AccuracyAbstract
This paper discusses, trying to accurately assess the price of Bitcoin by looking at differ-ent parameters affects the value of Bitcoin. In our work, we focus on understanding and seeing the evolution of Bitcoin daily market, a1 and gaining intuition in the most rele-vant aspects surrounding the Bitcoin price. In the meantime, market capitalization of publicly traded cryptocurrencies exceeds $ 230 billion. The most important cryptocur-rency, Bitcoin, is used primarily as a digital value store, and its pricing opportunities have been extensively considered. These features are described in more detail in the fol-lowing paragraph: details of the main Bitcoin, as described in the paper. Bitcoin is the most expensive digital currency in the market. However, Bitcoin prices have been highly volatile, making it difficult to forecast. As a result, the goal of this research is to find the most efficient and accurate model for predicting Bitcoin prices using various machine learning algorithms. Several regression models with scikit-learn and Keras libraries were tested using 1-minute interval trading data from the Bitcoin exchange website bit stamp from January 1. 2012 to January 8, 2018. The best results showed a Mean Squared Error (MSE) as low as 0.00002 and an R- Square (R2) as high as 99.2 percent.
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