# Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study

arXiv: https://arxiv.org/abs/2604.13928 · HTML: https://mlsanity.com/paper/2604.13928/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 15 Apr 2026.

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

- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models](https://mlsanity.com/paper/2112.09293/) (2021)
- [Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection](https://mlsanity.com/paper/2401.03341/) (2024)
- [Dimension Reduction for time series with Variational AutoEncoders](https://mlsanity.com/paper/2204.11060/) (2022)

## Abstract (arXiv)

Industrial time-series data from real production environments exhibits substantially higher complexity than commonly used benchmark datasets, primarily due to heterogeneous, multi-stage operational processes. As a result, anomaly detection methods validated under simplified conditions often fail to generalize to industrial settings. This work presents an empirical study on a unique dataset collected from fully operational industrial machinery, explicitly capturing pronounced process-induced variability. We evaluate which model classes are capable of capturing this complexity, starting with a classical Isolation Forest baseline and extending to multiple autoencoder architectures. Experimental results show that Isolation Forest is insufficient for modeling the non-periodic, multi-scale dynamics present in the data, whereas autoencoders consistently perform better. Among them, temporal convolutional autoencoders achieve the most robust performance, while recurrent and variational variants require more careful tuning.