The time-series anomaly detection is one of the most fundamental tasks for
time-series. Unlike the time-series forecasting and classification, the
time-series anomaly detection typically requires unsupervised (or
self-supervised) training since collecting and labeling anomalous observations
are difficult. In addition, most existing methods resort to limited forms of
anomaly measurements and therefore, it is not clear whether they are optimal in
all circumstances. To this end, we present a multivariate time-series anomaly
detector based on score-based generative models, called MadSGM, which considers
the broadest ever set of anomaly measurement factors: i) reconstruction-based,
ii) density-based, and iii) gradient-based anomaly measurements. We also design
a conditional score network and its denoising score matching loss for the
time-series anomaly detection. Experiments on five real-world benchmark
datasets illustrate that MadSGM achieves the most robust and accurate
predictions.

我们提出了一种基于评分的生成模型的多变量时间序列异常检测器，称为 MadSGM，它考虑了最广泛的异常度量因素：基于重建、基于密度和基于梯度的异常度量。通过在五个真实世界基准数据集上的实验证明，MadSGM 实现了最稳健和准确的预测。

基于打分的生成模型的多元异常检测

MadSGM: Multivariate Anomaly Detection with Score-based Generative  Models

Online class-incremental continual learning is a specific task of continual
learning. It aims to continuously learn new classes from data stream and the
samples of data stream are seen only once, which suffers from the catastrophic
forgetting issue, i.e., forgetting historical knowledge of old classes.
Existing replay-based methods effectively alleviate this issue by saving and
replaying part of old data in a proxy-based or contrastive-based replay manner.
Although these two replay manners are effective, the former would incline to
new classes due to class imbalance issues, and the latter is unstable and hard
to converge because of the limited number of samples. In this paper, we conduct
a comprehensive analysis of these two replay manners and find that they can be
complementary. Inspired by this finding, we propose a novel replay-based method
called proxy-based contrastive replay (PCR). The key operation is to replace
the contrastive samples of anchors with corresponding proxies in the
contrastive-based way. It alleviates the phenomenon of catastrophic forgetting
by effectively addressing the imbalance issue, as well as keeps a faster
convergence of the model. We conduct extensive experiments on three real-world
benchmark datasets, and empirical results consistently demonstrate the
superiority of PCR over various state-of-the-art methods.

本研究针对在线学习中的持续学习问题，提出了一种代理 - 对比重演（PCR）的新方法，通过解决不平衡问题和保持更快的收敛速度，有效缓解了历史知识丢失和忘记问题，并在三个真实数据集上实验验证了方法的有效性。