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We study the problem of the intraday short-term volume forecasting incryptocurrency exchange markets. The predictions are built by using transactionand order book data from different markets where the exchange takes place.Methodologically, we propose a temporal mixture ensemble, capable of adaptivelyexploiting, for the forecasting, different sources of data and providing avolume point estimate, as well as its uncertainty. We provide evidence of theoutperformance of our model by comparing its outcomes with those obtained withdifferent time series and machine learning methods. Finally, we discuss thepredictions conditional to volume and we find that also in this case machinelearning methods outperform econometric models.

Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets