Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets
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.
arXiv comment36 pages
References in corpus (14)
- Scikit-learn: Machine Learning in Python
- An overview of gradient descent optimization algorithms
- Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
- Accurate Uncertainties for Deep Learning Using Calibrated Regression
- Anticipating cryptocurrency prices using machine learning
- Social signals and algorithmic trading of Bitcoin
- A Simple Baseline for Bayesian Uncertainty in Deep Learning
- High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
- Recurrent Mixture Density Network for Spatiotemporal Visual Attention
- Gradient Descent Happens in a Tiny Subspace
- Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
- Why Do Markets Crash? Bitcoin Data Offers Unprecedented Insights
- Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders
- Gaussian Process Volatility Model