摘要翻译：
未来比特币价格的不确定性使得准确预测比特币价格变得困难。因此，准确预测比特币的价格对于加密货币市场中投资者和市场主体的决策过程具有重要意义。利用2012年01/01/012至2019年16/08/16/08的历史数据，使用机器学习技术（通过惩罚最大似然的广义线性模型、随机森林、带线性核的支持向量回归和堆叠集成）来预测比特币的价格。预测模型采用关键技术指标和高维技术指标作为预测指标。用平均绝对百分比误差(MAPE)、均方根误差(RMSE)、平均绝对误差(MAE)和决定系数（R-平方）来评价这些技术的性能。性能指标表明，采用双基学习器（随机森林和基于惩罚最大似然的广义线性模型）和以线性核为元学习器的支持向量回归的叠加集成模型是预测比特币价格的最优模型。叠加集成模型的MAPE、RMSE、MAE和R平方值分别为0.0191%、15.5331美元、124.5508美元和0.9967。这些数值显示了使用堆叠系综模型预测比特币价格的高度可靠性。准确预测比特币未来价格，将为加密货币市场的投资者和市场主体带来显著回报。
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英文标题：
《Are Bitcoins price predictable? Evidence from machine learning
  techniques using technical indicators》
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作者：
Samuel Asante Gyamerah
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最新提交年份：
2019
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  The uncertainties in future Bitcoin price make it difficult to accurately predict the price of Bitcoin. Accurately predicting the price for Bitcoin is therefore important for decision-making process of investors and market players in the cryptocurrency market. Using historical data from 01/01/2012 to 16/08/2019, machine learning techniques (Generalized linear model via penalized maximum likelihood, random forest, support vector regression with linear kernel, and stacking ensemble) were used to forecast the price of Bitcoin. The prediction models employed key and high dimensional technical indicators as the predictors. The performance of these techniques were evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared). The performance metrics revealed that the stacking ensemble model with two base learner (random forest and generalized linear model via penalized maximum likelihood) and support vector regression with linear kernel as meta-learner was the optimal model for forecasting Bitcoin price. The MAPE, RMSE, MAE, and R-squared values for the stacking ensemble model were 0.0191%, 15.5331 USD, 124.5508 USD, and 0.9967 respectively. These values show a high degree of reliability in predicting the price of Bitcoin using the stacking ensemble model. Accurately predicting the future price of Bitcoin will yield significant returns for investors and market players in the cryptocurrency market.
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PDF链接：
https://arxiv.org/pdf/1909.01268