# Periodic Graph-Enhanced Multivariate Time Series Anomaly Detector

arXiv: https://arxiv.org/abs/2509.17472 · HTML: https://mlsanity.com/paper/2509.17472/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 70 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Resurrecting Recurrent Neural Networks for Long Sequences](https://mlsanity.com/paper/2303.06349/) (2023)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://mlsanity.com/paper/1608.08242/) (2016)
- [Discrete Graph Structure Learning for Forecasting Multiple Time Series](https://mlsanity.com/paper/2101.06861/) (2021)
- [Multivariate Time-series Anomaly Detection via Graph Attention Network](https://mlsanity.com/paper/2009.02040/) (2020)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting](https://mlsanity.com/paper/2302.02051/) (2023)

## Abstract (arXiv)

Multivariate time series (MTS) anomaly detection commonly encounters in various domains like finance, healthcare, and industrial monitoring. However, existing MTS anomaly detection methods are mostly defined on the static graph structure, which fails to perform an accurate representation of complex spatio-temporal correlations in MTS. To address this issue, this study proposes a Periodic Graph-Enhanced Multivariate Time Series Anomaly Detector (PGMA) with the following two-fold ideas: a) designing a periodic time-slot allocation strategy based Fast Fourier Transform (FFT), which enables the graph structure to reflect dynamic changes in MTS; b) utilizing graph neural network and temporal extension convolution to accurate extract the complex spatio-temporal correlations from the reconstructed periodic graphs. Experiments on four real datasets from real applications demonstrate that the proposed PGMA outperforms state-of-the-art models in MTS anomaly detection.