Chainarong Amornbunchornvej, Elena Zheleva, Tanya Berger-Wolf
arXiv 1 Feb 2020 · Machine Learning · 1 citations (OpenAlex)
arXiv:2002.00208 · 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. The assumption of fixed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. 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 and Variable-lag Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allow causes to influence effects with arbitrary time delays. In addition, we propose methods for inferring both variable-lag Granger causality and Transfer Entropy relations. In our approaches, we utilize an optimal warping path of Dynamic Time Warping (DTW) to infer variable-lag causal relations. We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets. Our approaches can be applied in any domain of time series analysis. The software of this work is available in the R-CRAN package: VLTimeCausality.
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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 | Erdal Atukeren et al (2010) The relationship between the F-test and the Schwarz criterion: implications for Granger-causality tests | 1.000 | 8 | 5 | 100% |
| 2 | Chainarong Amornbunchornvej, Ivan Brugere, Ariana Strandburg-Peshkin… (2018) Coordination Event Detection and Initiator Identification in Time Series Data self | 1.000 | 7 | 4 | 100% |
| 3 | Simon Behrendt, Thomas Dimpfl, Franziska J. Peter, and David J. Zimm… (2019) RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy | 1.000 | 7 | 3 | 100% |
| 4 | Clive WJ Granger (1969) Investigating causal relations by econometric models and cross-spectral methods | 0.928 | 4 | 3 | 100% |
| 5 | Joon Lee, Shamim Nemati, Ikaro Silva, Bradley A Edwards, James P But… (2012) Transfer entropy estimation and directional coupling change detection in biomedical time series | 0.928 | 4 | 3 | 100% |
| 6 | Hiroaki Sakoe and Seibi Chiba (1978) Dynamic programming algorithm optimization for spoken word recognition | 0.843 | 4 | 3 | 75% |
| 7 | Lionel Barnett, Adam B. Barrett, and Anil K. Seth (2009) Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables | 0.843 | 3 | 3 | 100% |
| 8 | Andrew Arnold, Yan Liu, and Naoki Abe (2007) Temporal Causal Modeling with Graphical Granger Methods. In Proceedings of the 13th ACM SIGKDD International Conference on Knowl… | 0.811 | 4 | 2 | 100% |
| 9 | Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, and Michel B… (2015) Telling cause from effect in deterministic linear dynamical systems. In ICML. 285–294 | 0.811 | 4 | 2 | 100% |
| 10 | Thomas Dimpfl and Franziska Julia Peter (2013) Using transfer entropy to measure information flows between financial markets | 0.737 | 3 | 2 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
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| 1 | My Publication Title –- Multiple Authors | 1.000 | 5 | 3 |