arXiv 10 Sep 2023 · Statistics — Machine Learning · 1 citations (OpenAlex)
arXiv:2309.05107 · PDF · DOI · OpenAlex · Extracted main text
I introduce a novel algorithm and accompanying Python library, named mlcausality, designed for the identification of nonlinear Granger causal relationships. This novel algorithm uses a flexible plug-in architecture that enables researchers to employ any nonlinear regressor as the base prediction model. Subsequently, I conduct a comprehensive performance analysis of mlcausality when the prediction regressor is the kernel ridge regressor with the radial basis function kernel. The results demonstrate that mlcausality employing kernel ridge regression achieves competitive AUC scores across a diverse set of simulated data. Furthermore, mlcausality with kernel ridge regression yields more finely calibrated $p$-values in comparison to rival algorithms. This enhancement enables mlcausality to attain superior accuracy scores when using intuitive $p$-value-based thresholding criteria. Finally, mlcausality with the kernel ridge regression exhibits significantly reduced computation times compared to existing nonlinear Granger causality algorithms. In fact, in numerous instances, this innovative approach achieves superior solutions within computational timeframes that are an order of magnitude shorter than those required by competing algorithms.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Axel Wismüller, Adora M Dsouza, M Ali Vosoughi, and Anas Abidin (2021) Large-scale nonlinear granger causality for inferring directed dependence from short multivariate time-series data | 0.874 | 5 | 2 | 100% |
| 2 | Luiz A Baccalá and Koichi Sameshima (2001) Partial directed coherence: a new concept in neural structure determination | 0.405 | 1 | 1 | 100% |
| 3 | Liangyue Cao (1997) Practical method for determining the minimum embedding dimension of a scalar time series | 0.405 | 1 | 1 | 100% |
| 4 | Prince Joseph Erneszer Javier (2021) causal-ccm a Python implementation of Convergent Cross Mapping, 6 2021 | 0.405 | 1 | 1 | 100% |
| 5 | Wilfrid J Dixon and Alexander M Mood (1946) The statistical sign test | 0.405 | 1 | 1 | 100% |
| 6 | Adora M DSouza, Anas Z Abidin, Lutz Leistritz, and Axel Wismüller (2017) Exploring connectivity with large-scale granger causality on resting-state functional mri | 0.405 | 1 | 1 | 100% |
| 7 | Peter Exterkate, Patrick JF Groenen, Christiaan Heij, and Dick van D… (2016) Nonlinear forecasting with many predictors using kernel ridge regression | 0.405 | 1 | 1 | 100% |
| 8 | Zhong-Ke Gao, Michael Small, and Juergen Kurths (2017) Complex network analysis of time series | 0.405 | 1 | 1 | 100% |
| 9 | Clive WJ Granger (1969) Investigating causal relations by econometric models and cross-spectral methods | 0.405 | 1 | 1 | 100% |
| 10 | Lucas Lacasa, Vincenzo Nicosia, and Vito Latora (2015) Network structure of multivariate time series | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.