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Causality Learning from Time Series Data for the Industrial Finance Analysis via the Multi-Dimensional Point Process

Liangliang Shi1,2, Peili Lu3, Junchi Yan4,5,*

1 School of Computer Science and Software Engineering, East China Normal University, 200062, China
2 KLATASDS-MOE, East China Normal University, 200062, China
3 Minghuan Technology Co., Ltd., Shanghai, 200030, China
4 Shanghai Jiao Tong University, Shanghai, 200240, China
5 Shenzhen Institute of Artificial Intelligence and Robotics for Society, 518172, China

* Corresponding Author: Junchi Yan. Email:

Intelligent Automation & Soft Computing 2020, 26(5), 873-885.


Causality learning has been an important tool for decision making, especially for financial analytics. Given the time series data, most existing works construct the causality network with the traditional regression models and estimate the causality by pairs. To fulfil a holistic one-shot inference procedure over the whole network, we propose a new causal inference method for the multidimensional time series data, specifically related to some case studies for the industrial finance analytics. Specifically, the time series are first converted to the event sequences with timestamps by fluctuation the detection, and then a multidimensional point process is used for learning the underlying causality among the event sequences, which we assume stands for the relations among the time series. The expectation-maximization algorithm is used for minimizing the negative loglikelihood with the regularization in order to avoid overfitting in the high dimension and will make the causal inference more reasonable. Over 250 factors with time series data related to the industrial finance are used in this paper to evaluate the model and the experimental showcase of the superiority of our approach on the real-world finance data.


Cite This Article

L. Shi, P. Lu and J. Yan, "Causality learning from time series data for the industrial finance analysis via the multi-dimensional point process," Intelligent Automation & Soft Computing, vol. 26, no.5, pp. 873–885, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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