Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies
Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies
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DOI：
10.3390/jrfm14100486
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发表时间：
2021-10
影响因子：
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通讯作者：
D. Miller;Jong-Min Kim
中科院分区：
文献类型：
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作者：
D. Miller;Jong-Min Kim
在这项研究中，我们使用了单变量和多变量机器学习方法，如递归神经网络、深度学习神经网络、Holt指数平滑、自回归整合移动平均、ForecastX和长期短期记忆网络，预测了基于市值的前十大加密货币的对数收益。在预测误差度量方面，多变量长短期记忆网络比单变量机器学习方法表现得更好。
In this study, we predicted the log returns of the top 10 cryptocurrencies based on market cap, using univariate and multivariate machine learning methods such as recurrent neural networks, deep learning neural networks, Holt’s exponential smoothing, autoregressive integrated moving average, ForecastX, and long short-term memory networks. The multivariate long short-term memory networks performed better than the univariate machine learning methods in terms of the prediction error measures.