Chainarong Amornbunchornvej, Elena Zheleva, Tanya Y. Berger-Wolf
arXiv 18 Dec 2019 · Machine Learning · 2 citations (OpenAlex)
arXiv:1912.10829 · PDF · DOI · OpenAlex · Extracted main text
Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop variable-lag Granger causality, a generalization of Granger causality that relaxes the assumption of the fixed time delay and allows causes to influence effects with arbitrary time delays. In addition, we propose a method for inferring variable-lag Granger causality relations. We demonstrate our approach on an application for studying coordinated collective behavior and show that it performs better than several existing methods in both simulated and real-world datasets. Our approach can be applied in any domain of time series analysis.
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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 | E. Atukeren et al., “The relationship between the f-test and the sch… (2010) The relationship between the f-test and the schwarz criterion: implications for granger-causality tests | 1.000 | 7 | 4 | 100% |
| 2 | =2 plus 43 minus 4 C. Amornbunchornvej, I. Brugere, A. Strandburg-Pe… (2018) Coordination event detection and initiator identification in time series data self | 1.000 | 5 | 4 | 100% |
| 3 | N. Shajarisales, D. Janzing, B. Schölkopf, and M. Besserve, “Telling… (2015) Telling cause from effect in deterministic linear dynamical systems | 1.000 | 5 | 3 | 100% |
| 4 | C. W. Granger, “Investigating causal relations by econometric models… (1969) Investigating causal relations by econometric models and cross-spectral methods | 0.843 | 3 | 3 | 100% |
| 5 | =2 plus 43 minus 4 A. Arnold, Y. Liu, and N. Abe, “Temporal causal m… (2007) Temporal causal modeling with graphical granger methods | 0.737 | 3 | 2 | 100% |
| 6 | Y. Liu, T. Bahadori, and H. Li, “Sparse-gev: Sparse latent space mod… (2012) Sparse-gev: Sparse latent space model for multivariate extreme value time serie modeling | 0.737 | 3 | 2 | 100% |
| 7 | H. Sakoe and S. Chiba, “Dynamic programming algorithm optimization f… (1978) Dynamic programming algorithm optimization for spoken word recognition | 0.737 | 3 | 2 | 100% |
| 8 | J. Pearl, “Causality: Models, reasoning and inference cambridge univ… (2000) Causality: Models, reasoning and inference cambridge university press | 0.644 | 2 | 2 | 100% |
| 9 | A. Gretton, K. Fukumizu, C. H. Teo, L. Song, B. Schölkopf, and A. J.… (2008) A kernel statistical test of independence | 0.511 | 2 | 1 | 100% |
| 10 | C. J. Quinn, N. Kiyavash, and T. P. Coleman, “Directed information g… (2015) Directed information graphs | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 37 scored citations.