Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume
- Journal Article
Rights / licenseCreative Commons Attribution 4.0 International
We study the problem of the intraday short-term volume forecasting in cryptocurrency multi-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 clear outperformance of our model with respect to econometric models. Moreover our model performs slightly better than Gradient Boosting Machine while having a much clearer interpretability of the results. Finally, we show that the above results are robust also when restricting the prediction analysis to each volume quartile. Show more
Journal / seriesDecisions in Economics and Finance
Pages / Article No.
SubjectEconometrics; Machine learning; Cryptocurrency markets; Temporal mixture ensemble
Organisational unit03784 - Helbing, Dirk / Helbing, Dirk
02045 - Dep. Geistes-, Sozial- u. Staatswiss. / Dep. of Humanities, Social and Pol.Sc.
00002 - ETH Zürich
871042 - SoBigData++: An Integrated Infrastructures for Social Mining and Big Data Analytics (EC)
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