Bitcoin is a decentralized cryptocurrency, which is a type of digital asset that provides the basis for peer-to-peer financial transactions based on blockchain technology. One of the main problems with decentralized cryptocurrencies is price volatility, which indicates the need for studying the underlying price model. Moreover, Bitcoin prices exhibit non-stationary behavior, where the statistical distribution of data changes over time. This paper demonstrates high-performance machine learning-based classification and regression models for predicting Bitcoin price movements and prices in short and medium terms. In previous works, machine learning-based classification has been studied for an only one-day time frame, while this work goes beyond that by using machine learning-based models for one, seven, thirty and ninety days. The developed models are feasible and have high performance, with the classification models scoring up to 65% accuracy for next-day forecast and scoring from 62 to 64% accuracy for seventh–ninetieth-day forecast. For daily price forecast, the error percentage is as low as 1.44%, while it varies from 2.88 to 4.10% for horizons of seven to ninety days. These results indicate that the presented models outperform the existing models in the literature.

比特币是一种去中心化的加密货币，它是一种数字资产，为基于区块链技术的点对点金融交易提供了基础。分散式加密货币的主要问题之一是价格波动，这表明需要研究基础价格模型。此外，比特币价格表现出非平稳行为，数据的统计分布随时间变化。本文演示了基于高性能机器学习的分类和回归模型，用于预测中短期比特币价格走势和价格。在以前的工作中，仅基于一天的时间范围研究了基于机器学习的分类，而这项工作超出了使用基于机器学习的模型进行1天，7天，30天和90天的分类的范围。所开发的模型是可行的，并且具有较高的性能，分类模型对次日预报的准确度最高为65％，对第七至第九天预报的准确度为62至64％。对于每日价格预测，误差百分比低至1.44％，而对于7天到90天的误差范围则从2.88到4.10％不等。这些结果表明，所提出的模型优于文献中的现有模型。