Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies

被引：9

作者：

Miller, Dante ^{[1
]}

Kim, Jong-Min ^{[1
]}

机构：

[1] Univ Minnesota Morris, Div Sci & Math, Stat Discipline, Morris, MN 56267 USA

来源：

JOURNAL OF RISK AND FINANCIAL MANAGEMENT | 2021年 / 14卷 / 10期

关键词：

cryptocurrencies; deep learning networks; recurrent neural networks; long short-term memory networks; VOLATILITY; GARCH; BITCOIN;

D O I：

10.3390/jrfm14100486

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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.

引用

页数：10

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