论文标题
基于NLP的分类时间序列异常检测
NLP Based Anomaly Detection for Categorical Time Series
论文作者
论文摘要
在大型多维时间序列中识别异常是整个多个领域的至关重要且艰巨的任务。文献中很少有方法可以解决某些变量本质上的分类时解决此任务。我们通过实施和测试基于IT的三个不同的机器学习异常检测和根本原因研究模型来形式化分类时间序列和经典自然语言处理之间的类比，并证明了这种比喻的异常检测和根本原因研究的强度。
Identifying anomalies in large multi-dimensional time series is a crucial and difficult task across multiple domains. Few methods exist in the literature that address this task when some of the variables are categorical in nature. We formalize an analogy between categorical time series and classical Natural Language Processing and demonstrate the strength of this analogy for anomaly detection and root cause investigation by implementing and testing three different machine learning anomaly detection and root cause investigation models based upon it.
