تقرير
Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders
| العنوان: | Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders |
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| المؤلفون: | Calderon, Josuan, Berman, Gordon J. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.03212 |
| رقم الأكسشن: | edsarx.2406.03212 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |