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Investigating SINDy as a Tool for Causal Discovery in Time Series Signals
Conference proceeding

Investigating SINDy as a Tool for Causal Discovery in Time Series Signals

Andrew O'Brien, Rosina Weber and Edward Kim
Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998), v 2023-, pp 1-5
04 Jun 2023
DOI: https://doi.org/10.1109/ICASSP49357.2023.10096965

Abstract

Acoustics causality dynamical systems Heuristic algorithms Mathematical models Signal processing Signal processing algorithms SINDy Sparsity System identification Time series analysis
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.

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