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

TL;DR

This paper introduces a temporal mixture ensemble model for short-term intraday volume forecasting in cryptocurrency markets, leveraging transaction and order book data to improve accuracy and quantify uncertainty.

Contribution

The paper presents a novel adaptive ensemble model that combines multiple data sources for more accurate volume predictions in cryptocurrency trading.

Findings

The proposed model outperforms traditional time series and machine learning methods.

Machine learning methods outperform econometric models in volume prediction.

The model provides both point estimates and uncertainty quantification.

Abstract

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.