NLP Based Anomaly Detection for Categorical Time Series
- URL: http://arxiv.org/abs/2204.10483v1
- Date: Fri, 22 Apr 2022 03:53:51 GMT
- Title: NLP Based Anomaly Detection for Categorical Time Series
- Authors: Matthew Horak and Sowmya Chandrasekaran and Giovanni Tobar
- Abstract summary: We formalize an analogy between categorical time series and classical Natural Language Processing.
We implement and test three different machine learning anomaly detection and root cause investigation models based upon it.
- Score: 7.895459735927415
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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