Abstract
The content of this chapter is organized as follows: The problem formulation is presented in Sect. 10.1. Section 10.2 introduces the details about our proposed approach and its equivalent Bayesian model. A solution capable of online inference with particle learning is given in Sect. 10.3. Extensive empirical evaluation is demonstrated in Sect. 10.4. Finally, we conclude our work and discuss the future work.
Part of this chapter is in the paper titled “Online Discovery for Stable and Grouping Causalities in Multivariate Time Series” by Wentao Wang, Bin Shi et al. (2018) presently under review for publication.
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References
S. Castruccio, Assessing the spatio-temporal structure of annual and seasonal surface temperature for CMIP5 and reanalysis. Spatial Stat. 18, 179–193 (2016)
B. Carpentieri, I.S. Duff, L. Giraud, Sparse pattern selection strategies for robust frobenius-norm minimization preconditioners in electromagnetism. Numer. Linear Algebr. Appl. 7(7–8), 667–685 (2000)
C.M. Carvalho, M.S. Johannes, H.F. Lopes, N.G. Polson, Particle learning and smoothing. Stat. Sci. 25, 88–106 (2010)
P.M. Djuric, J.H. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M.F. Bugallo, J. Miguez, Particle filtering. IEEE Signal Process. Mag. 20(5), 19–38 (2003)
A.C. Harvey, Forecasting, Structural Time Series Models and the Kalman Filter (Cambridge University Press, Cambridge, 1990)
W. Kleiber, R.W. Katz, B. Rajagopalan et al., Daily minimum and maximum temperature simulation over complex terrain. Ann. Appl. Stat. 7(1), 588–612 (2013)
Q. Li, N. Lin, The Bayesian elastic net. Bayesian Anal. 5(1), 151–170 (2010)
A.C. Lozano, H. Li, A. Niculescu-Mizil, Y. Liu, C. Perlich, J. Hosking, N. Abe, Spatial-temporal causal modeling for climate change attribution, in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (ACM, New York, 2009), pp. 587–596
K.P. Murphy, Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, MA, 2012)
H. Zou, T. Hastie, Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat Methodol. 67(2), 301–320 (2005)
C. Zeng, Q. Wang, W. Wang, T. Li, L. Shwartz, Online inference for time-varying temporal dependency discovery from time series, in 2016 IEEE International Conference on Big Data (Big Data) (IEEE, Piscataway, 2016), pp. 1281–1290
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Shi, B., Iyengar, S.S. (2020). Online Discovery for Stable and Grouping Causalities in Multivariate Time Series. In: Mathematical Theories of Machine Learning - Theory and Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-17076-9_10
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