用价格波动数据提升比特币走势预测准确率。
A Machine Learning Approach For Bitcoin Forecasting
- 融合开盘、最高、最低价构建时序特征，提升预测方向性。
- 最低价配合门控循环单元与基线模型的集成方法表现最佳。
- 适合关注金融时间序列预测的开发者和量化研究者。
比特币是近年来日益流行的加密货币。先前研究指出，仅依赖收盘价不足以准确预测股市序列。本文引入一组新的时间序列特征，并证明其中部分特征对提升方向准确性至关重要。实验表明，开盘价、最高价和最低价构成的关键特征组合能显著改善预测效果，其中最低价在与门控循环单元（GRU）网络及基线预测模型集成后贡献最大。非价格相关的比特币特征影响可忽略。所提方法在方向准确性上达到当前最优水平。
原文摘要 · Abstract (English)
Bitcoin is one of the cryptocurrencies that is gaining more popularity in recent years. Previous studies have shown that closing price alone is not enough to forecast stock market series. We introduce a new set of time series and demonstrate that a subset is necessary to improve directional accuracy based on a machine learning ensemble. In our experiments, we study which time series and machine learning algorithms deliver the best results. We found that the most relevant time series that contribute to improving directional accuracy are Open, High and Low, with the largest contribution of Low in combination with an ensemble of Gated Recurrent Unit network and a baseline forecast. The relevance of other Bitcoin-related features that are not price-related is negligible. The proposed method delivers similar performance to the state-of-the-art when observing directional accuracy.
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