Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
<|reference_start|>Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection: 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.<|reference_end|>
@article{fu2024generating,
    title={Generating Fine-Grained Causality in Climate Time Series Data for
  Forecasting and Anomaly Detection},
    author={Dongqi Fu, Yada Zhu, Hanghang Tong, Kommy Weldemariam, Onkar Bhardwaj,
  Jingrui He},
    journal={arXiv preprint arXiv:2408.04254},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04254},
    primaryClass={cs.LG}
}