Department of Computer Graphics, Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100, Gliwice, Akademicka 16, Poland.
Department of Applied Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100, Gliwice, Akademicka 16, Poland.
Sci Data. 2023 Feb 7;10(1):79. doi: 10.1038/s41597-023-01978-7.
The ability to uncover characteristics based on empirical measurement is an important step in understanding the underlying system that gives rise to an observed time series. This is especially important for biological signals whose characteristic contributes to the underlying dynamics of the physiological processes. Therefore, by studying such signals, the physiological systems that generate them can be better understood. The datasets presented consist of 33,000 time series of 15 dynamical systems (five chaotic and ten non-chaotic) of the first, second, or third order. Here, the order of a dynamical system means its dimension. The non-chaotic systems were divided into the following classes: periodic, quasi-periodic, and non-periodic. The aim is to propose datasets for machine learning methods, in particular deep learning techniques, to analyze unknown dynamical system characteristics based on obtained time series. In technical validation, three classifications experiments were conducted using two types of neural networks with long short-term memory modules and convolutional layers.
基于经验测量揭示特征的能力是理解产生观测时间序列的基础系统的重要步骤。这对于生物信号尤其重要，因为其特征有助于生理过程的基础动态。因此，通过研究这些信号，可以更好地理解产生它们的生理系统。所呈现的数据集由 33000 个时间序列组成，这些时间序列来自五个一阶、二阶或三阶的 15 个动力系统（五个混沌和十个非混沌）。这里，动力系统的阶数意味着其维度。非混沌系统分为以下几类：周期性、准周期性和非周期性。目的是为机器学习方法，特别是深度学习技术，提出数据集，以便根据获得的时间序列分析未知动力系统的特征。在技术验证中，使用带有长短时记忆模块和卷积层的两种神经网络进行了三次分类实验。
