Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization

异常检测 杠杆(统计) 计算机科学 机器学习 自编码 水准点(测量) 特征学习 深度学习 数据挖掘 人工智能 时间序列 多元统计 代表(政治) 模式识别(心理学) 异步通信 政治 计算机网络 政治学 法学 地理 大地测量学
作者
Ahmed Abdulaal,Zhuang‐Hua Liu,Tomer Lancewicki
出处
期刊:Knowledge Discovery and Data Mining 卷期号:: 2485-2494 被引量:133
标识
DOI:10.1145/3447548.3467174
摘要

Engineers at eBay utilize robust methods in monitoring IT system signals for anomalies. However, the growing scale of signals, both in volumes and dimensions, overpowers traditional statistical state-space or supervised learning tools. Thus, state-of-the-art methods based on unsupervised deep learning are sought in recent research. However, we experienced flaws when implementing those methods, such as requiring partial supervision and weaknesses to high dimensional datasets, among other reasons discussed in this paper. We propose a practical approach for inferring anomalies from large multivariate sets. We observe an abundance of time series in real-world applications, which exhibit asynchronous and consistent repetitive variations, such as IT, weather, utility, and transportation. Our solution is designed to leverage this behavior. The solution utilizes spectral analysis on the latent representation of a pre-trained autoencoder to extract dominant frequencies across the signals, which are then used in a subsequent network that learns the phase shifts across the signals and produces a synchronized representation of the raw multivariate. Random subsets of the synchronous multivariate are then fed into an array of autoencoders learning to minimize the quantile reconstruction losses, which are then used to infer and localize anomalies based on a majority vote. We benchmark this method against state-of-the-art approaches on public datasets and eBay's data using their referenced evaluation methods. Furthermore, we address the limitations of the referenced evaluation methods and propose a more realistic evaluation method.
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