论文标题
在时间序列中无监督异常检测的降级结构
Denoising Architecture for Unsupervised Anomaly Detection in Time-Series
论文作者
论文摘要
时间序列的异常提供了各个行业的关键方案的见解，从银行和航空航天到信息技术，安全和医学。但是，由于对异常的定义，经常缺乏标签以及此类数据中存在的极为复杂的时间相关性，因此识别时间序列数据中的异常尤其具有挑战性。 LSTM自动编码器是基于长期短期内存网络的异常检测的编码器传统方案，该方案学会重建时间序列行为，然后使用重建错误来识别异常。我们将Denoising Architecture作为对此LSTM编码模型模型的补充，并研究其对现实世界以及人为生成的数据集的影响。我们证明，所提出的体系结构既提高了准确性和训练速度，从而使LSTM自动装置器更有效地用于无监督的异常检测任务。
Anomalies in time-series provide insights of critical scenarios across a range of industries, from banking and aerospace to information technology, security, and medicine. However, identifying anomalies in time-series data is particularly challenging due to the imprecise definition of anomalies, the frequent absence of labels, and the enormously complex temporal correlations present in such data. The LSTM Autoencoder is an Encoder-Decoder scheme for Anomaly Detection based on Long Short Term Memory Networks that learns to reconstruct time-series behavior and then uses reconstruction error to identify abnormalities. We introduce the Denoising Architecture as a complement to this LSTM Encoder-Decoder model and investigate its effect on real-world as well as artificially generated datasets. We demonstrate that the proposed architecture increases both the accuracy and the training speed, thereby, making the LSTM Autoencoder more efficient for unsupervised anomaly detection tasks.
