Discovery of causal time intervals

Zhenhui Li, Guanjie Zheng, Amal Agarwal, Lingzhou Xue, Thomas Claude Yves Lauvaux

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Causality analysis, beyond "mere" correlations, has become increasingly important for scientific discoveries and policy decisions. Many of these real-world applications involve time series data. A key observation is that the causality between time series could vary significantly over time. For example, a rain could cause severe traffic jams during the rush hours, but has little impact on the traffic at midnight. However, previous studies mostly look at the whole time series when determining the causal relationship between them. Instead, we propose to detect the partial time intervals with causality. As it is time consuming to enumerate all time intervals and test causality for each interval, we further propose an efficient algorithm that can avoid unnecessary computations based on the bounds of F-test in the Granger causality test. We use both synthetic datasets and real datasets to demonstrate the efficiency of our pruning techniques and that our method can effectively discover interesting causal intervals in the time series data.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
EditorsNitesh Chawla, Wei Wang
PublisherSociety for Industrial and Applied Mathematics Publications
Pages804-812
Number of pages9
ISBN (Electronic)9781611974874
StatePublished - Jan 1 2017
Event17th SIAM International Conference on Data Mining, SDM 2017 - Houston, United States
Duration: Apr 27 2017Apr 29 2017

Publication series

NameProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017

Other

Other17th SIAM International Conference on Data Mining, SDM 2017
CountryUnited States
CityHouston
Period4/27/174/29/17

Fingerprint

Time series
Rain

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications

Cite this

Li, Z., Zheng, G., Agarwal, A., Xue, L., & Lauvaux, T. C. Y. (2017). Discovery of causal time intervals. In N. Chawla, & W. Wang (Eds.), Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017 (pp. 804-812). (Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017). Society for Industrial and Applied Mathematics Publications.
Li, Zhenhui ; Zheng, Guanjie ; Agarwal, Amal ; Xue, Lingzhou ; Lauvaux, Thomas Claude Yves. / Discovery of causal time intervals. Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017. editor / Nitesh Chawla ; Wei Wang. Society for Industrial and Applied Mathematics Publications, 2017. pp. 804-812 (Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017).
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Li, Z, Zheng, G, Agarwal, A, Xue, L & Lauvaux, TCY 2017, Discovery of causal time intervals. in N Chawla & W Wang (eds), Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017. Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017, Society for Industrial and Applied Mathematics Publications, pp. 804-812, 17th SIAM International Conference on Data Mining, SDM 2017, Houston, United States, 4/27/17.

Discovery of causal time intervals. / Li, Zhenhui; Zheng, Guanjie; Agarwal, Amal; Xue, Lingzhou; Lauvaux, Thomas Claude Yves.

Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017. ed. / Nitesh Chawla; Wei Wang. Society for Industrial and Applied Mathematics Publications, 2017. p. 804-812 (Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Causality analysis, beyond "mere" correlations, has become increasingly important for scientific discoveries and policy decisions. Many of these real-world applications involve time series data. A key observation is that the causality between time series could vary significantly over time. For example, a rain could cause severe traffic jams during the rush hours, but has little impact on the traffic at midnight. However, previous studies mostly look at the whole time series when determining the causal relationship between them. Instead, we propose to detect the partial time intervals with causality. As it is time consuming to enumerate all time intervals and test causality for each interval, we further propose an efficient algorithm that can avoid unnecessary computations based on the bounds of F-test in the Granger causality test. We use both synthetic datasets and real datasets to demonstrate the efficiency of our pruning techniques and that our method can effectively discover interesting causal intervals in the time series data.

AB - Causality analysis, beyond "mere" correlations, has become increasingly important for scientific discoveries and policy decisions. Many of these real-world applications involve time series data. A key observation is that the causality between time series could vary significantly over time. For example, a rain could cause severe traffic jams during the rush hours, but has little impact on the traffic at midnight. However, previous studies mostly look at the whole time series when determining the causal relationship between them. Instead, we propose to detect the partial time intervals with causality. As it is time consuming to enumerate all time intervals and test causality for each interval, we further propose an efficient algorithm that can avoid unnecessary computations based on the bounds of F-test in the Granger causality test. We use both synthetic datasets and real datasets to demonstrate the efficiency of our pruning techniques and that our method can effectively discover interesting causal intervals in the time series data.

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M3 - Conference contribution

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Li Z, Zheng G, Agarwal A, Xue L, Lauvaux TCY. Discovery of causal time intervals. In Chawla N, Wang W, editors, Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017. Society for Industrial and Applied Mathematics Publications. 2017. p. 804-812. (Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017).