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SHREC: A Physics-Based Machine Learning Approach to Time Series Analysis and Causal Driver Reconstruction
In modern data science, time series analysis is a cornerstone for understanding dynamic systems. From predicting market trends to unraveling biological processes, time series data provides insights into how systems evolve over time. However, one of the most challenging aspects of time series analysis is reconstructing causal drivers—the unobserved variables that govern a system’s behavior.
By leveraging the principles of dynamical systems and topological data analysis, SHREC enables accurate reconstruction of causal drivers, even in noisy and incomplete datasets. SHREC addresses these limitations by combining physics-based principles with machine learning to infer unmeasured causal drivers from observed response data. Its physics-based foundation, combined with innovative machine learning techniques, enables accurate and efficient reconstruction of causal drivers across diverse datasets.
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