  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.
