Liangliang Shi1,2, Peili Lu3, Junchi Yan4,5,*
Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 873-885, 2020, DOI:10.32604/iasc.2020.010121
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… More >
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