# VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge

arXiv: https://arxiv.org/abs/2409.14816 · HTML: https://mlsanity.com/paper/2409.14816/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction](https://mlsanity.com/paper/1705.05690/) (2017)
- [TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT](https://mlsanity.com/paper/2303.03611/) (2023)
- [Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things](https://mlsanity.com/paper/1712.06343/) (2017)
- [Demand Forecasting in Smart Grid Using Long Short-Term Memory](https://mlsanity.com/paper/2107.13653/) (2021)

## Abstract (arXiv)

Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demanding, requiring cloud architectures prone to latency and bandwidth issues. This work presents VARADE, a novel solution implementing a light autoregressive framework based on variational inference, which is best suited for real-time execution on the edge. The proposed approach was validated on a robotic arm, part of a pilot production line, and compared with several state-of-the-art algorithms, obtaining the best trade-off between anomaly detection accuracy, power consumption and inference frequency on two different edge platforms.