We introduce an anomaly detection method for multivariate time series data with the aim of identifying critical periods and features influencing extreme climate events like snowmelt in the Arctic. This method leverages the Variational Autoencoder (VAE) integrated with dynamic thresholding and correlation-based feature clustering. This framework enhances the VAE's ability to identify localized dependencies and learn the temporal relationships in climate data, thereby improving the detection of anomalies as demonstrated by its higher F1-score on benchmark datasets. The study's main contributions include the development of a robust anomaly detection method, improving feature representation within VAEs through clustering, and creating a dynamic threshold algorithm for localized anomaly detection. This method offers explainability of climate anomalies across different regions.

翻译：我们提出了一种针对多元时间序列数据的异常检测方法，旨在识别影响北极融雪等极端气候事件的关键时段和特征。该方法利用变分自编码器（VAE），并结合了动态阈值与基于相关性的特征聚类。该框架增强了VAE识别局部依赖关系和学习气候数据中时序关系的能力，从而提高了异常检测性能，其在基准数据集上更高的F1分数证明了这一点。本研究的主要贡献包括：开发了一种鲁棒的异常检测方法，通过聚类改进了VAE内部的特征表示，并创建了一种用于局部异常检测的动态阈值算法。该方法能够对不同区域的气候异常提供可解释性分析。