论文标题
在加密货币交易市场中预测的室内销量预测的时间混合组合模型
Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets
论文作者
论文摘要
我们研究加密货币交易所市场中日内短期预测的问题。这些预测是通过使用交易所发生的不同市场的交易和订单数据来构建的。从方法论上讲，我们提出了一个时间混合集合，能够自适应地利用预测的数据来源，并提供体积点估计值及其不确定性。我们通过将模型的结果与不同时间序列和机器学习方法获得的结果进行比较，提供了模型的表现。最后，我们讨论了有条件的预测，我们发现在这种情况下，机器学习方法的表现要优于计量经济学模型。
We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the outperformance of our model by comparing its outcomes with those obtained with different time series and machine learning methods. Finally, we discuss the predictions conditional to volume and we find that also in this case machine learning methods outperform econometric models.
