论文标题
TISAT：时间序列异常变压器
TiSAT: Time Series Anomaly Transformer
论文作者
论文摘要
虽然时间序列的异常检测一直是研究的积极研究领域，但最近的方法采用了不足的评估标准，导致F1得分膨胀。我们表明，就这个流行但错误的评估标准而言，一种基本的随机猜测方法可以胜过最先进的探测器。在这项工作中，我们提出了一个适当的评估指标，以测量检测顺序异常的及时性和精度。此外，大多数现有方法无法从长序列中捕获时间特征。已经证明，基于自我注意力的方法（例如变形金刚）在捕获长期依赖性方面特别有效，同时在训练和推理期间在计算上有效。我们还提出了一种有效的变压器方法，用于时间序列中的异常检测，并广泛评估我们在几个流行的基准数据集上提出的方法。
While anomaly detection in time series has been an active area of research for several years, most recent approaches employ an inadequate evaluation criterion leading to an inflated F1 score. We show that a rudimentary Random Guess method can outperform state-of-the-art detectors in terms of this popular but faulty evaluation criterion. In this work, we propose a proper evaluation metric that measures the timeliness and precision of detecting sequential anomalies. Moreover, most existing approaches are unable to capture temporal features from long sequences. Self-attention based approaches, such as transformers, have been demonstrated to be particularly efficient in capturing long-range dependencies while being computationally efficient during training and inference. We also propose an efficient transformer approach for anomaly detection in time series and extensively evaluate our proposed approach on several popular benchmark datasets.
