NLP Based Anomaly Detection for Categorical Time Series
This addresses a gap in anomaly detection for categorical time series, which is incremental as it adapts existing NLP methods to a specific domain.
The authors tackled the problem of detecting anomalies in multi-dimensional time series with categorical variables by drawing an analogy to NLP, implementing and testing three machine learning models for anomaly detection and root cause investigation.
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.