Robust Causal Discovery in Real-World Time Series with Power-Laws
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908838525927424 |
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| author | Tusoni, Matteo Masi, Giuseppe Coletta, Andrea Glielmo, Aldo Arrigoni, Viviana Bartolini, Novella |
| author_facet | Tusoni, Matteo Masi, Giuseppe Coletta, Andrea Glielmo, Aldo Arrigoni, Viviana Bartolini, Novella |
| contents | Exploring causal relationships in stochastic time series is a challenging yet crucial task with a vast range of applications, including finance, economics, neuroscience, and climate science. Many algorithms for Causal Discovery (CD) have been proposed; however, they often exhibit a high sensitivity to noise, resulting in spurious causal inferences in real data. In this paper, we observe that the frequency spectra of many real-world time series follow a power-law distribution, notably due to an inherent self-organizing behavior. Leveraging this insight, we build a robust CD method based on the extraction of power-law spectral features that amplify genuine causal signals. Our method consistently outperforms state-of-the-art alternatives on both synthetic benchmarks and real-world datasets with known causal structures, demonstrating its robustness and practical relevance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12257 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Causal Discovery in Real-World Time Series with Power-Laws Tusoni, Matteo Masi, Giuseppe Coletta, Andrea Glielmo, Aldo Arrigoni, Viviana Bartolini, Novella Machine Learning Data Analysis, Statistics and Probability Other Statistics Exploring causal relationships in stochastic time series is a challenging yet crucial task with a vast range of applications, including finance, economics, neuroscience, and climate science. Many algorithms for Causal Discovery (CD) have been proposed; however, they often exhibit a high sensitivity to noise, resulting in spurious causal inferences in real data. In this paper, we observe that the frequency spectra of many real-world time series follow a power-law distribution, notably due to an inherent self-organizing behavior. Leveraging this insight, we build a robust CD method based on the extraction of power-law spectral features that amplify genuine causal signals. Our method consistently outperforms state-of-the-art alternatives on both synthetic benchmarks and real-world datasets with known causal structures, demonstrating its robustness and practical relevance. |
| title | Robust Causal Discovery in Real-World Time Series with Power-Laws |
| topic | Machine Learning Data Analysis, Statistics and Probability Other Statistics |
| url | https://arxiv.org/abs/2507.12257 |