Investigating Sindy As a Tool For Causal Discovery In Time Series Signals
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 30 references that are in the map. First posted on arXiv 29 Dec 2022.
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6 of 30 references.
References · 6
- 2017Detecting causal associations in large nonlinear time series datasetscited by 240
- 2017Causal Structure Learningcited by 84
- 2019A unified sparse optimization framework to learn parsimonious physics-informed models from datacited by 49
- 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
- 2020Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Datacited by 36
- 2020Causal models for dynamical systemscited by 34
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Citing papers per year: 2024: 2.
2 papers.