Within the larger field of real-world time series analysis, one of the most important tasks is the assessment of their stochastic vs. chaotic nature, and not surprisingly, many metrics and algorithms have been proposed to this end. A still under-explored option is offered by Deep Learning, i.e. a family of machine learning algorithms that perform automatic feature extraction and (usually supervised) classification. We here propose a series of numerical experiments aimed at assessing the performance of different Deep Learning models in discriminating between stochastic and chaotic time series generated by discrete maps, and at comparing such performance with that of standard metrics in the literature. Deep Learning clearly outperforms other alternatives, both in terms of minimum time series length and resilience to observational noise, and can be used to define a new gold standard against which old and new methods can be compared. At the same time, we explore more general considerations about the use of Deep Learning, including whether such models are able to detect general chaoticity fingerprints, or only patterns associated to specific chaotic maps; and what steps ought to be taken to make Deep Learning models a feasible instrument.

在现实世界时间序列分析的更大领域中，最重要的任务之一是评估它们的随机性与混沌性，毫不奇怪，为此目的提出了许多指标和算法。深度学习提供了一个尚未开发的选项，即执行自动特征提取和（通常是监督的）分类的一系列机器学习算法。我们在这里提出了一系列数值实验，旨在评估不同深度学习模型在区分离散映射生成的随机和混沌时间序列方面的性能，并将这种性能与文献中的标准度量进行比较。深度学习在最小时间序列长度和对观测噪声的弹性方面明显优于其他替代方案，并且可以用来定义一个新的黄金标准，可以比较新旧方法。同时，我们探讨了有关使用深度学习的更一般的考虑，包括此类模型是否能够检测一般混沌指纹，或者仅检测与特定混沌图相关的模式；以及应该采取哪些步骤使深度学习模型成为一种可行的工具。