Single-particle traces of the diffusive motion of molecules, cells, or animals are by-now routinely measured, similar to stochastic records of stock prices or weather data. Deciphering the stochastic mechanism behind the recorded dynamics is vital in understanding the observed systems. Typically, the task is to decipher the exact type of diffusion and/or to determine system parameters. The tools used in this endeavor are currently revolutionized by modern machine-learning techniques. In this Perspective we provide an overview over recently introduced methods in machine-learning for diffusive time series, most notably, those successfully competing in the Anomalous-Diffusion-Challenge. As such methods are often criticized for their lack of interpretability, we focus on means to include uncertainty estimates and feature-based approaches, both improving interpretability and providing concrete insight into the learning process of the machine. We expand the discussion by examining predictions on different out-of-distribution data. We also comment on expected future developments.

翻译：单分子、细胞或动物的扩散运动单粒子轨迹现已常规测量，类似于股票价格或天气数据的随机记录。解读记录动力学背后的随机机制对于理解所观测系统至关重要。通常，任务在于解读确切的扩散类型和/或确定系统参数。现代机器学习技术正彻底革新用于此目标的工具。在本展望中，我们概述了近期引入的用于扩散时间序列的机器学习方法，尤以在反常扩散挑战赛中成功竞争的方法为重点。由于此类方法常因缺乏可解释性而受到批评，我们聚焦于纳入不确定性估计和基于特征的方法，这两者均能提升可解释性，并为机器的学习过程提供具体洞见。我们通过检验在不同分布外数据上的预测来扩展讨论，并评述了预期的未来发展。