Chakattrai Sookkongwaree, Tattep Lakmuang, Chainarong Amornbunchornvej
arXiv 1 Aug 2025 · Artificial Intelligence
arXiv:2508.00658 · PDF · DOI · OpenAlex · Extracted main text
Understanding causal relationships in time series is fundamental to many domains, including neuroscience, economics, and behavioral science. Granger causality is one of the well-known techniques for inferring causality in time series. Typically, Granger causality frameworks have a strong fix-lag assumption between cause and effect, which is often unrealistic in complex systems. While recent work on variable-lag Granger causality (VLGC) addresses this limitation by allowing a cause to influence an effect with different time lags at each time point, it fails to account for the fact that causal interactions may vary not only in time delay but also across frequency bands. For example, in brain signals, alpha-band activity may influence another region with a shorter delay than slower delta-band oscillations. In this work, we formalize Multi-Band Variable-Lag Granger Causality (MB-VLGC) and propose a novel framework that generalizes traditional VLGC by explicitly modeling frequency-dependent causal delays. We provide a formal definition of MB-VLGC, demonstrate its theoretical soundness, and propose an efficient inference pipeline. Extensive experiments across multiple domains demonstrate that our framework significantly outperforms existing methods on both synthetic and real-world datasets, confirming its broad applicability to any type of time series data. Code and datasets are publicly available.
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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 | Amornbunchornvej, C.; Zheleva, E.; and Berger-Wolf, T (2021) Variable-Lag Granger Causality and Transfer Entropy for Time Series Analysis self | 1.000 | 5 | 3 | 100% |
| 2 | Granger, C. W (1969) Investigating causal relations by econometric models and cross-spectral methods | 0.737 | 3 | 2 | 100% |
| 3 | Behrendt, S.; Dimpfl, T.; Peter, F. J.; and Zimmermann, D. J (2019) RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy | 0.644 | 2 | 2 | 100% |
| 4 | Runge, J (2020) Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets | 0.644 | 2 | 2 | 100% |
| 5 | Sakoe, H.; and Chiba, S (1978) Dynamic programming algorithm optimization for spoken word recognition | 0.405 | 1 | 1 | 100% |
| 6 | Geweke, J (1982) Measurement of linear dependence and feedback between multiple time series | 0.405 | 1 | 1 | 100% |
| 7 | Schreiber, T (2000) Measuring information transfer | 0.405 | 1 | 1 | 100% |
| 8 | Azzalini, A.; and Bowman, A. W (1990) A look at some data on the Old Faithful geyser | 0.405 | 1 | 1 | 100% |
| 9 | Bastos, A. M.; and Schoffelen, J.-M (2015) A tutorial review of functional connectivity analysis methods and their interpretational pitfalls | 0.405 | 1 | 1 | 100% |
| 10 | Box, G. E.; Jenkins, G. M.; Reinsel, G. C.; and Ljung, G. M (2015) Time series analysis: forecasting and control | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.