论文标题
基于预测的多变量时间序列异常检测的多光值框架
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection
论文作者
论文摘要
当今的网络世界非常多。在极端品种中收集的指标需要多元算法正确检测异常。但是，正如广泛验证的方法一样，基于预测的算法通常在跨数据集中表现次优或不一致。一个关键的常见问题是他们努力成为一定程度的所有问题，但异常在本质上是独特的。我们提出了一种定制这种区别的方法。呈现FMUAD-基于预测的，多种多样的，无监督的异常检测框架。 FMUAD明确捕获了异常类型的特征 - 空间变化，时间变化和相关性变化与独立模块。然后，这些模块共同学习了最佳特征表示，该特征表示高度灵活和直观，与类别中的大多数模型不同。广泛的实验表明，我们的FMUAD框架始终优于其他基于预测的异常检测器。
Today's cyber-world is vastly multivariate. Metrics collected at extreme varieties demand multivariate algorithms to properly detect anomalies. However, forecast-based algorithms, as widely proven approaches, often perform sub-optimally or inconsistently across datasets. A key common issue is they strive to be one-size-fits-all but anomalies are distinctive in nature. We propose a method that tailors to such distinction. Presenting FMUAD - a Forecast-based, Multi-aspect, Unsupervised Anomaly Detection framework. FMUAD explicitly and separately captures the signature traits of anomaly types - spatial change, temporal change and correlation change - with independent modules. The modules then jointly learn an optimal feature representation, which is highly flexible and intuitive, unlike most other models in the category. Extensive experiments show our FMUAD framework consistently outperforms other state-of-the-art forecast-based anomaly detectors.
