Short-term prediction for Ethereum with Deep Neural Networks and Statistical Validation Tests

Abstract

Cryptocurrency has become a popular asset in global financial markets, meaning that individual investors and asset management companies worldwide are considering this new investment class. The main contribution of this research is to address an intra-day forecasting problem with hourly granularity by comparing deep network architectures, including ones with attention mechanisms for the Ethereum intrinsic cryptocurrency (ETH). Since variations on the deep learning model parameter values may also introduce variability in the results produced by the models, different statistical validations were considered part of the comparison process. Finally, this work shows that the Temporal Convolutional Network model (TCN) outperformed other architectures considered for a short-term forecast period in terms of processing time. The TCN deep learning model is also amongst the most accurate models, using an auto-regressive integrated moving average model (ARIMA) as a baseline.