Understanding the causal interaction of time series variables can contribute to time series data analysis for many real-world applications, such as climate forecasting and extreme weather alerts. However, causal relationships are difficult to be fully observed in real-world complex settings, such as spatial-temporal data from deployed sensor networks. Therefore, to capture fine-grained causal relations among spatial-temporal variables for further a more accurate and reliable time series analysis, we first design a conceptual fine-grained causal model named TBN Granger Causality, which adds time-respecting Bayesian Networks to the previous time-lagged Neural Granger Causality to offset the instantaneous effects. Second, we propose an end-to-end deep generative model called TacSas, which discovers TBN Granger Causality in a generative manner to help forecast time series data and detect possible anomalies during the forecast. For evaluations, besides the causality discovery benchmark Lorenz-96, we also test TacSas on climate benchmark ERA5 for climate forecasting and the extreme weather benchmark of NOAA for extreme weather alerts.

本研究针对复杂环境中因果关系难以充分观测的问题，提出了一种名为TBN Granger因果关系的细粒度因果模型，通过结合时间尊重的贝叶斯网络和时滞神经Granger因果关系，提升时间序列分析的准确性。研究结果表明，提出的深度生成模型TacSas能够有效发现因果关系，进一步提升气候预测和极端天气预警的效果。

气候时间序列数据中生成细粒度因果关系用于预测和异常检测