用因果关系提升北极海冰预测准确率
Learning What Matters: Causal Time Series Modeling for Arctic Sea Ice Prediction
- 结合格兰杰因果与PCMCI+筛选关键影响因子
- 43年数据验证，预测精度和可解释性双提升
- 适合需要可靠预测的气候与环境研究者
传统机器学习依赖相关性，难以区分真实因果与虚假关联，限制了模型的鲁棒性、可解释性和泛化能力。为此，本文提出一种融合多变量格兰杰因果（MVGC）与PCMCI+的因果特征选择方法，构建混合神经架构。基于1979至2021年共43年的北极海冰范围（SIE）及海洋-大气变量的逐日与逐月数据，该方法识别出对SIE动态具有因果影响的关键预测因子，优先选择直接原因，减少冗余特征，提升计算效率。实验表明，引入因果输入后，不同预报时效下的预测准确率与可解释性均得到改善。该框架不仅适用于北极海冰预测，还可推广至其他高维动态系统，为因果驱动的预测建模提供理论支持与实践路径。
原文摘要 · Abstract (English)
Conventional machine learning and deep learning models typically rely on correlation-based learning, which often fails to distinguish genuine causal relationships from spurious associations, limiting their robustness, interpretability, and ability to generalize. To overcome these limitations, we introduce a causality-aware deep learning framework that integrates Multivariate Granger Causality (MVGC) and PCMCI+ for causal feature selection within a hybrid neural architecture. Leveraging 43 years (1979-2021) of Arctic Sea Ice Extent (SIE) data and associated ocean-atmospheric variables at daily and monthly resolutions, the proposed method identifies causally influential predictors, prioritizes direct causes of SIE dynamics, reduces unnecessary features, and enhances computational efficiency. Experimental results show that incorporating causal inputs leads to improved prediction accuracy and interpretability across varying lead times. While demonstrated on Arctic SIE forecasting, the framework is broadly applicable to other dynamic, high-dimensional domains, offering a scalable approach that advances both the theoretical foundations and practical performance of causality-informed predictive modeling.
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