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.

翻译：我们的工作呈现了两项基本贡献。在应用方面，我们解决了预测次日加密货币价格这一具有挑战性的问题。在方法论方面，我们提出了一种新的动态建模方法。该方法既保留了状态空间模型的概率框架（能够对估计结果进行不确定性量化），又融合了深度神经网络的函数逼近能力，我们称之为深度状态空间模型。实验基于主流加密货币数据（源自雅虎财经），以预测次日价格为研究目标。通过与最先进动态建模技术及经典方法的基准对比，结果表明所提方法在预测精度上取得了最优综合表现。