  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.
