  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.
