Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders
<|reference_start|>Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders: Most approaches for assessing causality in complex dynamical systems fail when the interactions between variables are inherently non-linear and non-stationary. Here we introduce Temporal Autoencoders for Causal Inference (TACI), a methodology that combines a new surrogate data metric for assessing causal interactions with a novel two-headed machine learning architecture to identify and measure the direction and strength of time-varying causal interactions. Through tests on both synthetic and real-world datasets, we demonstrate TACI's ability to accurately quantify dynamic causal interactions across a variety of systems. Our findings display the method's effectiveness compared to existing approaches and also highlight our approach's potential to build a deeper understanding of the mechanisms that underlie time-varying interactions in physical and biological systems.<|reference_end|>
@article{calderon2024inferring,
    title={Inferring the time-varying coupling of dynamical systems with temporal
  convolutional autoencoders},
    author={Josuan Calderon and Gordon J. Berman},
    journal={arXiv preprint arXiv:2406.03212},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03212},
    primaryClass={cs.LG q-bio.QM}
}