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.
翻译:时间序列中的异常现象提供了从银行和航空到信息技术、安全和医学等一系列行业的关键情景的洞察力。然而,由于对异常现象的定义不准确、标签的经常缺乏以及这些数据中存在的极其复杂的时间相关性,查明时间序列数据中的异常现象尤其具有挑战性。 LSTM Autoencoder 是一种基于长期短期记忆网络的异常检测的计算器-代号计划,它学会了重建时间序列行为,然后利用重建错误来识别异常现象。我们引入了Denoising 架构,作为LSTM Encoder-Decoder模型的补充,并调查其对现实世界的影响以及人为生成的数据集。我们证明,拟议的架构提高了准确性和培训速度,从而使 LSTM Autocuder更高效地完成不受监控的异常检测任务。