Deep State-Space Model for Predicting Cryptocurrency Price
This work addresses price prediction for cryptocurrency traders, but it appears incremental as it combines existing probabilistic and neural network methods.
The paper tackles the problem of predicting day-ahead cryptocurrency prices by proposing a deep state-space model, which achieves the best overall accuracy compared to state-of-the-art and classical techniques.
Our work presents two fundamental contributions. On the application side, we tackle the challenging problem of predicting day-ahead crypto-currency prices. On the methodological side, a new dynamical modeling approach is proposed. Our approach keeps the probabilistic formulation of the state-space model, which provides uncertainty quantification on the estimates, and the function approximation ability of deep neural networks. We call the proposed approach the deep state-space model. The experiments are carried out on established cryptocurrencies (obtained from Yahoo Finance). The goal of the work has been to predict the price for the next day. Benchmarking has been done with both state-of-the-art and classical dynamical modeling techniques. Results show that the proposed approach yields the best overall results in terms of accuracy.