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.

我们的工作提出了两个基本贡献：在应用方面，我们解决了预测次日加密货币价格的难题；在方法上，我们提出了一种新的动态建模方法，深度状态空间模型，它保持了状态空间模型的概率形式，提供了估计的不确定性量化，并具备深度神经网络的函数逼近能力。实验使用了从 Yahoo Finance 获取的已建立的加密货币进行，旨在预测下一天的价格。与现有技术和传统动态建模技术进行了基准测试，结果显示所提出的方法在准确性方面取得了最佳综合结果。