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

2020/05/19 by Nino Antulov-Fantulin, Tian Guo, Antulov-Fantulin, Nino +3
Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Financial Risk and Volatility Modeling #Financial Markets and Investment Strategies

paper · pdf · doi:10.48550/arxiv.2005.09356

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.

Citations

• An overview of gradient descent optimization algorithms
• Gradient Descent Happens in a Tiny Subspace
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• Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

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