Discovering Causal Structures from Time Series Data via Enhanced Granger Causality

  • Ling LuoEmail author
  • Wei Liu
  • Irena Koprinska
  • Fang Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9457)


Granger causality has been applied to explore predictive causal relations among multiple time series in various fields. However, the existence of non-stationary distributional changes among the time series variables poses significant challenges. By analyzing a real dataset, we observe that factors such as noise, distribution changes and shifts increase the complexity of the modelling, and large errors often occur when the underlying distribution shifts with time.

Motivated by this challenge, we propose a new regression model for causal structure discovery – a Linear Model with Weighted Distribution Shift (linear WDS), which improves the prediction accuracy of the Granger causality model by taking into account the weights of the distribution-shift samples and by optimizing a quadratic-mean based objective function. The linear WDS is integrated in the Granger causality model to improve the inference of the predictive causal structure. The performance of the enhanced Granger causality model is evaluated on synthetic datasets and real traffic datasets, and the proposed model is compared with three different regression-based Granger causality models (standard linear regression, robust regression and quadratic-mean-based regression). The results show that the enhanced Granger causality model outperforms the other models especially when there are distribution shifts in the data.


Data mining algorithms Causal inference Time series regression 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ling Luo
    • 1
    • 2
    Email author
  • Wei Liu
    • 2
    • 3
  • Irena Koprinska
    • 1
  • Fang Chen
    • 2
  1. 1.School of Information TechnologiesUniversity of SydneySydneyAustralia
  2. 2.Australian Technology Park LaboratoryNICTASydneyAustralia
  3. 3.Faculty of Engineering and Information TechnologiesUniversity of Technology SydneySydneyAustralia

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