【摘要】 多元时间序列异常检测是数据挖掘领域中的一项重要应用.基于深度学习的异常检测方法已经取得了重大进展，但其仍然存在一定的局限性.首先，是它们假设训练数据仅由正常数据组成，而忽略了异常数据可能导致的不可预测性；其次，大部分方法并未考虑到时间序列的独特特性.为了解决上述问题，基于预训练提出了一种新颖的用于多元时间序列的异常检测框架.框架由预训练模块和预测模块组成，首先预训练模块通过学习时间序列的密集向量表示，增强其可预测性，然后预测模块中充分利用时间序列的独特特性捕获其时间依赖.通过广泛的实验证明了所提出的模型的有效性，在三个真实数据集上均显著优于最先进的模型.更多还原

【Abstract】 Multivariate time series anomaly detection is an essential application in data mining. Recent deep learning-based anomaly detection methods have made significant progress, but they still have some limitations. Firstly, they assume that the training data consists of only normal data and ignore the unpredictability that may result from anomalous data.Secondly, most of the methods do not take into account the unique characteristics of time series. To address the above problems, a novel anomaly detection framework for multivariate time series is proposed based on pre-training. The framework consists of a pre-training module and a prediction module. Firstly, the pre-training module enhances the predictability of the time series by learning its dense vector representation. Then the unique properties of the time series are fully exploited in the prediction module to capture its time dependence. The effectiveness of the proposed model is demonstrated through extensive experiments, significantly outperforming the state-of-the-art model on all three real data sets.更多还原