A new method for Causal Discovery (CD) in noisy real-world time series by leveraging the inherent power-law distribution observed in frequency spectra, leading to more robust causal inferences.
Exploring causal relationships in stochastic time series is challenging due to high sensitivity to noise, often leading to spurious causal inferences. This research introduces a novel Causal Discovery method that exploits the observation that many real-world time series exhibit power-law frequency spectra, driven by self-organizing behavior. By extracting and amplifying these power-law spectral features, the method constructs a robust framework for causal inference. The proposed approach consistently outperforms state-of-the-art alternatives on both synthetic benchmarks and real-world datasets, demonstrating practical relevance.
Theoretical Link Between Scaling Laws and Weight Spectra in Shallow Neural Networks
ReadCluster-Aware Causal Mixer for Real-Time Anomaly Detection in Multivariate Time Series
ReadUnified Topological Framework for AI Representation Analysis: Introducing Symmetric Divergence and N
Read