提出新方法，提前预测未来异常事件概率。
Long-Term Outlier Prediction Through Outlier Score Modeling
- 分两层：先检测异常，再建模异常时间结构预测未来
- 在合成数据上同时实现精准检测与长期预测
- 无需特定模型，适合做异常预测的基准方法
本研究针对时间序列异常检测中的关键空白，提出一种新型问题设定——长期异常预测。传统方法主要关注即时检测，难以预测远期异常事件。为此，我们提出一种简单且无监督的两层方法，不依赖具体模型。第一层执行标准异常检测，第二层基于已观测异常的时间结构，预测未来异常得分。该框架不仅支持逐点检测，还可长期预测异常可能性。在合成数据集上的实验表明，该方法在检测与预测任务中表现优异。结果表明，该方法可作为未来异常检测与预测研究的强基线。
原文摘要 · Abstract (English)
This study addresses an important gap in time series outlier detection by proposing a novel problem setting: long-term outlier prediction. Conventional methods primarily focus on immediate detection by identifying deviations from normal patterns. As a result, their applicability is limited when forecasting outlier events far into the future. To overcome this limitation, we propose a simple and unsupervised two-layer method that is independent of specific models. The first layer performs standard outlier detection, and the second layer predicts future outlier scores based on the temporal structure of previously observed outliers. This framework enables not only pointwise detection but also long-term forecasting of outlier likelihoods. Experiments on synthetic datasets show that the proposed method performs well in both detection and prediction tasks. These findings suggest that the method can serve as a strong baseline for future work in outlier detection and forecasting.
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