@Article{iasc.2020.010121,
AUTHOR = {Liangliang Shi, Peili Lu, Junchi Yan},
TITLE = {Causality Learning from Time Series Data for the Industrial Finance Analysis via the Multi-Dimensional Point Process},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {5},
PAGES = {873--885},
URL = {http://www.techscience.com/iasc/v26n5/40810},
ISSN = {2326-005X},
ABSTRACT = {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.},
DOI = {10.32604/iasc.2020.010121}
}