论文标题
用于可解释异常检测的统计和深度学习的混合模型
Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection
论文作者
论文摘要
在两个预测任务上，混合方法已显示出优于纯统计和纯净的深度学习方法，并量化与这些预测相关的不确定性（预测间隔）。一个示例是多元指数平滑长期记忆（MES-LSTM），这是多元统计预测模型与经常性神经网络变体之间的混合体。还显示出（$ i $）产生准确的预测，并且（$ ii $）能够令人满意地量化相关的预测不确定性，可以成功地适应适合异常检测任务的模型。随着多元数据和新的应用领域的普遍性的增加，近年来提出了许多异常检测方法。 The proposed methods have largely focused on deep learning techniques, which are prone to suffer from challenges such as ($i$) large sets of parameters that may be computationally intensive to tune, $(ii)$ returning too many false positives rendering the techniques impractical for use, $(iii)$ requiring labeled datasets for training which are often not prevalent in real life, and ($iv$) understanding of the root深度学习方法的主要黑盒性质抑制了异常发生的原因。在本文中，提出了MES-LSTM的扩展，这是一种克服这些挑战的可解释异常检测模型。以可再生能源产生为应用领域，提出的方法是针对最先进的。发现MES-LSTM异常检测器至少在异常检测任务下至少与基准竞争，并且比基准测试效果更不容易从伪造效应中学习，从而使其在根本原因发现和解释中更可靠。
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at both forecasting tasks, and at quantifying the uncertainty associated with those forecasts (prediction intervals). One example is Multivariate Exponential Smoothing Long Short-Term Memory (MES-LSTM), a hybrid between a multivariate statistical forecasting model and a Recurrent Neural Network variant, Long Short-Term Memory. It has also been shown that a model that ($i$) produces accurate forecasts and ($ii$) is able to quantify the associated predictive uncertainty satisfactorily, can be successfully adapted to a model suitable for anomaly detection tasks. With the increasing ubiquity of multivariate data and new application domains, there have been numerous anomaly detection methods proposed in recent years. The proposed methods have largely focused on deep learning techniques, which are prone to suffer from challenges such as ($i$) large sets of parameters that may be computationally intensive to tune, $(ii)$ returning too many false positives rendering the techniques impractical for use, $(iii)$ requiring labeled datasets for training which are often not prevalent in real life, and ($iv$) understanding of the root causes of anomaly occurrences inhibited by the predominantly black-box nature of deep learning methods. In this article, an extension of MES-LSTM is presented, an interpretable anomaly detection model that overcomes these challenges. With a focus on renewable energy generation as an application domain, the proposed approach is benchmarked against the state-of-the-art. The findings are that MES-LSTM anomaly detector is at least competitive to the benchmarks at anomaly detection tasks, and less prone to learning from spurious effects than the benchmarks, thus making it more reliable at root cause discovery and explanation.
