# NLP Based Anomaly Detection for Categorical Time Series

arXiv: https://arxiv.org/abs/2204.10483 · HTML: https://mlsanity.com/paper/2204.10483/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 22 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Entity Embeddings of Categorical Variables](https://mlsanity.com/paper/1604.06737/) (2016)
- [Tabular Transformers for Modeling Multivariate Time Series](https://mlsanity.com/paper/2011.01843/) (2020)

## Built on by (most cited first, 2 of 2)

- [Analyzing categorical time series with the R package ctsfeatures](https://mlsanity.com/paper/2304.12332/) (2023)
- [DrafTS: Time-Aware Decomposition with Residual Correction for Time Series Modeling](https://mlsanity.com/paper/2609.33368/) (2026)

## Abstract (arXiv)

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.