论文标题
使用专家校正的比特币价格预测性建模
Bitcoin Price Predictive Modeling Using Expert Correction
论文作者
论文摘要
本文研究了比特币价格的线性模型，其中包括基于比特币货币统计，采矿过程，Google搜索趋势，Wikipedia页面访问的回归特征。与价格时间序列相比，回归模型预测与实际价格的偏差模式更为简单。假定这种模式可以由经验丰富的专家预测。通过这种方式，将回归模型和专家校正的组合结合起来，与回归模型或专家意见相比，人们可以获得更好的结果。结果表明，贝叶斯方法可以使用带有脂肪尾巴的分布来利用概率方法，并考虑比特币价格时间序列中的离群值。
The paper studies the linear model for Bitcoin price which includes regression features based on Bitcoin currency statistics, mining processes, Google search trends, Wikipedia pages visits. The pattern of deviation of regression model prediction from real prices is simpler comparing to price time series. It is assumed that this pattern can be predicted by an experienced expert. In such a way, using the combination of the regression model and expert correction, one can receive better results than with either regression model or expert opinion only. It is shown that Bayesian approach makes it possible to utilize the probabilistic approach using distributions with fat tails and take into account the outliers in Bitcoin price time series.
