NTS NOTEARS: Learning Nonparametric Temporal DAGs With Time Series Data and Prior Knowledge
We propose a score based DAG structure learning method for time series data that captures linear, nonlinear, lagged and instantaneous relations among variables while ensuring acyclicity throughout the entire graph. The proposed method extends nonparametric NOTEARS (Zheng et al. 2020) a recent continuous optimization approach for learning nonparametric instantaneous DAGs. The proposed method is faster than constraint based methods using nonlinear conditional independence tests. We also promote the use of optimization constraints to incorporate prior knowledge into the structure learning process. A broad set of experiments with simulated data demonstrates that the proposed method discovers better DAG structures than several recent comparison methods. We also evaluate the proposed method on complex real world data acquired from NHL ice hockey games containing a mixture of continuous and discrete variables. The code is available at https:/github.com/xiangyu sun 789/ NTS NOTEARS/
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