Evaluating anomaly detection algorithms in time series data is critical as
inaccuracies can lead to flawed decision-making in various domains where
real-time analytics and data-driven strategies are essential. Traditional
performance metrics assume iid data and fail to capture the complex temporal
dynamics and specific characteristics of time series anomalies, such as early
and delayed detections. We introduce Proximity-Aware Time series anomaly
Evaluation (PATE), a novel evaluation metric that incorporates the temporal
relationship between prediction and anomaly intervals. PATE uses
proximity-based weighting considering buffer zones around anomaly intervals,
enabling a more detailed and informed assessment of a detection. Using these
weights, PATE computes a weighted version of the area under the Precision and
Recall curve. Our experiments with synthetic and real-world datasets show the
superiority of PATE in providing more sensible and accurate evaluations than
other evaluation metrics. We also tested several state-of-the-art anomaly
detectors across various benchmark datasets using the PATE evaluation scheme.
The results show that a common metric like Point-Adjusted F1 Score fails to
characterize the detection performances well, and that PATE is able to provide
a more fair model comparison. By introducing PATE, we redefine the
understanding of model efficacy that steers future studies toward developing
more effective and accurate detection models.

评估时间序列数据中的异常检测算法是至关重要的，因为不准确的检测可能导致各种实时分析和数据驱动策略中的错误决策。我们引入了 Proximity-Aware Time series anomaly Evaluation (PATE)，这是一种新颖的评估指标，它考虑了预测和异常区间之间的时间关系，并使用基于接近度的加权方法来计算精确率和召回率曲线下面积的加权版本。我们的实验表明，PATE 在提供比其他评估指标更合理和准确的评估方面具有优势，我们还使用 PATE 评估方案在各种基准数据集上对几种最先进的异常检测器进行了测试，结果表明，像点调整 F1 分数这样的常见指标无法很好地表征检测性能，而 PATE 能够提供更公平的模型比较。通过引入 PATE，我们重新定义了模型效能的理解，从而引导未来的研究朝着开发更有效和准确的检测模型的方向发展。

PATE：接近感知的时间序列异常评估

PATE: Proximity-Aware Time series anomaly Evaluation

For a long time, research on time series anomaly detection has mainly focused
on finding outliers within a given time series. Admittedly, this is consistent
with some practical problems, but in other practical application scenarios,
people are concerned about: assuming a standard time series is given, how to
judge whether another test time series deviates from the standard time series,
which is more similar to the problem discussed in one-class classification
(OCC). Therefore, in this article, we try to re-understand and define the time
series anomaly detection problem through OCC, which we call 'time series
anomaly state detection problem'. We first use stochastic processes and
hypothesis testing to strictly define the 'time series anomaly state detection
problem', and its corresponding anomalies. Then, we use the time series
classification dataset to construct an artificial dataset corresponding to the
problem. We compile 38 anomaly detection algorithms and correct some of the
algorithms to adapt to handle this problem. Finally, through a large number of
experiments, we fairly compare the actual performance of various time series
anomaly detection algorithms, providing insights and directions for future
research by researchers.

通过一类分类 (OCC) 方法重新定义时间序列异常检测问题，使用随机过程和假设检验严格定义 “时间序列异常状态检测问题” 和其对应的异常，构建人工数据集并测试 38 个异常检测算法，通过大量实验比较了各种算法的实际性能，为未来研究提供了洞察和方向。

通过一类分类理解时间序列异常状态检测

Understanding Time Series Anomaly State Detection through One-Class  Classification

Data augmentation is a promising technique for unsupervised anomaly detection
in industrial applications, where the availability of positive samples is often
limited due to factors such as commercial competition and sample collection
difficulties. In this paper, how to effectively select and apply data
augmentation methods for unsupervised anomaly detection is studied. The impact
of various data augmentation methods on different anomaly detection algorithms
is systematically investigated through experiments. The experimental results
show that the performance of different industrial image anomaly detection
(termed as IAD) algorithms is not significantly affected by the specific data
augmentation method employed and that combining multiple data augmentation
methods does not necessarily yield further improvements in the accuracy of
anomaly detection, although it can achieve excellent results on specific
methods. These findings provide useful guidance on selecting appropriate data
augmentation methods for different requirements in IAD.

数据增强是工业应用中无监督异常检测的一种有前途的技术，本文通过实验系统研究了如何选择和应用数据增强方法来提高工业图像异常检测算法的性能，并发现不同数据增强方法对不同的算法并没有显著影响。这些发现为选择适当的数据增强方法提供了有用的指导。