论文标题
调查辛迪作为时间序列信号的因果发现工具
Investigating Sindy As a Tool For Causal Discovery In Time Series Signals
论文作者
论文摘要
Sindy算法已成功地用于识别时间序列数据中动态系统的管理方程。在本文中，我们认为这使Sindy成为因果发现的潜在有用的工具，并且可用于因果发现的现有工具可大幅提高Sindy作为强大稀疏建模和系统识别工具的性能。然后，我们从经验上证明，通过因果发现中的工具来增强信德算法可以为工程师提供一种学习因果关系强大的管理方程式的工具。
The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. In this paper, we argue that this makes SINDy a potentially useful tool for causal discovery and that existing tools for causal discovery can be used to dramatically improve the performance of SINDy as tool for robust sparse modeling and system identification. We then demonstrate empirically that augmenting the SINDy algorithm with tools from causal discovery can provides engineers with a tool for learning causally robust governing equations.
