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, 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/.
翻译:我们建议对时间序列数据采用基于分数的DAG结构学习方法,该方法可以捕捉各变量之间的线性、非线性、滞后和瞬时关系,同时确保整个图表的周期性。拟议方法扩展了非参数性Ontaras,这是最近一项用于学习非参数性瞬时DAG的连续优化方法。建议的方法比使用非线性有条件独立测试的制约性方法要快。我们还提倡使用优化限制,将先前的知识纳入结构学习过程。一系列模拟数据的广泛实验表明,拟议方法发现DAG结构比最近几项比较方法要好。我们还评估了从NHEL冰球游戏获得的复杂真实世界数据的拟议方法,该方法包含连续和离散变量的混合物。该代码可在https://github.comxiangyu-sun-789/NTS-NOTEES/上查阅。