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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)

Abstract

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.

Keywords

Data mining algorithms Causal inference Time series regression 

References

  1. 1.
    Granger, C.W.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica J. Econometric Soc. 37, 424–438 (1969)CrossRefGoogle Scholar
  2. 2.
    Hiemstra, C., Jones, J.D.: Testing for linear and nonlinear granger causality in the stock price-volume relation. J. Finance 49, 1639–1664 (1994)Google Scholar
  3. 3.
    Lozano, A.C., Li, H., Niculescu-Mizil, A., Liu, Y., Perlich, C., Hosking, J., Abe, N.: Spatial-temporal causal modeling for climate change attribution. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 587–596. ACM, 1557086 (2009)Google Scholar
  4. 4.
    Roebroeck, A., Formisano, E., Goebel, R.: Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage 25, 230–242 (2005)CrossRefGoogle Scholar
  5. 5.
    Diebold, F.: Elements of Forecasting. Cengage Learning, Mason (2006)Google Scholar
  6. 6.
    Liu, W., Chawla, S.: A quadratic mean based supervised learning model for managing data skewness. In: SDM, pp. 188–198 (2011)Google Scholar
  7. 7.
    Liu, X., Wu, X., Wang, H., Zhang, R., Bailey, J., Ramamohanarao, K.: Mining distribution change in stock order streams. In: 2010 IEEE 26th International Conference on Data Engineering, pp. 105–108 (2010)Google Scholar
  8. 8.
    Ristanoski, G., Liu, W., Bailey, J.: A time-dependent enhanced support vector machine for time series regression. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 946–954. ACM (2013)Google Scholar
  9. 9.
    Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46, 44 (2014)CrossRefzbMATHGoogle Scholar
  10. 10.
    Harchaoui, Z., Moulines, E., Bach, F.R.: Kernel change-point analysis. In: Advances in Neural Information Processing Systems, pp. 609–616 (2009)Google Scholar
  11. 11.
    Branch, M.A., Coleman, T.F., Li, Y.: A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems. SIAM J. Sci. Comput. 21, 1–23 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Seth, A.K.: A MATLAB toolbox for Granger causal connectivity analysis. J. Neurosci. Methods 186, 262–273 (2010)CrossRefGoogle Scholar
  13. 13.
    Baccalá, L.A., Sameshima, K.: Partial directed coherence: a new concept in neural structure determination. Biol. Cybern. 84, 463–474 (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Arnold, A., Liu, Y., Abe, N.: Temporal causal modeling with graphical granger methods. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 66–75. ACM (2007)Google Scholar
  15. 15.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar

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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