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awesome-multivariate-time-series-anomaly-detection-algorithms

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This repository contains a reading list of papers on multivariate time series anomaly detection. This repository is still being continuously improved.

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# Awesome Multivariate Time Series anomaly detection Papers
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## Description
This repository contains a reading list of papers on **multivariate time series anomaly detection**. **This repository is still being continuously improved.**

## Citation

🚩 2026/7/21: **If you find this repository helpful for your research, please consider citing the following paper:**

> **Zezhong Li**, Ling Wang (2026). **JT3D: Joint temporal segmentation for common pattern discovery in 3D tensor time series via segmented Gaussian modeling and information-gain**. *Neurocomputing, 682*, 133453. https://doi.org/10.1016/j.neucom.2026.133453.

> **Zezhong Li**, Wei Guo, Jianpeng An, Qi Wang, Yingchun Mei, Rongshun Juan, Tianshu Wang, Yang Li, and Zhongke Gao, **AD2T: Multivariate Time Series Anomaly Detection with Association Discrepancy Dual-Decoder Transformer,** *IEEE Sensors Journal (IEEE Sens J),* 2025. https://ieeexplore.ieee.org/document/10906418.

> Ling Wang, Qinchun Xu, **Zezhong Li**, **STANAP: Spatiotemporal Adaptive Neighborhood-Aware Prediction with Multimodal Evolution,** *Knowledge-Based Systems (KBS),* 2026. https://doi.org/10.1016/j.knosys.2026.116346.

```bibtex
@article{LI2026JT3D,
  title = {JT3D: Joint temporal segmentation for common pattern discovery in 3D tensor time series via segmented Gaussian modeling and information-gain},
  author = {Zezhong Li and Ling Wang},
  journal = {Neurocomputing},
  volume = {682},
  pages = {133453},
  year = {2026},
  issn = {0925-2312},
  doi = {10.1016/j.neucom.2026.133453},
  url = {https://www.sciencedirect.com/science/article/pii/S0925231226008507}
}

@ARTICLE{Li2025AD2T,
  author={Li, Zezhong and Guo, Wei and An, Jianpeng and Wang, Qi and Mei, Yingchun and Juan, Rongshun and Wang, Tianshu and Li, Yang and Gao, Zhongke},
  journal={IEEE Sensors Journal}, 
  title={AD2T: Multivariate Time-Series Anomaly Detection With Association Discrepancy Dual-Decoder Transformer}, 
  year={2025},
  volume={25},
  number={7},
  pages={11710-11721},
  keywords={Anomaly detection;Sensors;Transformers;Training;Time series analysis;Long short term memory;Correlation;Servers;Sensor phenomena and characterization;Measurement;Anomaly detection;association discrepancy;dual-decoder;multivariate time series (MTS);transformer},
doi={10.1109/JSEN.2025.3543835}}

@article{WANG2026STANAP,
title = {STANAP: Spatiotemporal adaptive neighborhood-aware prediction with multimodal evolution},
journal = {Knowledge-Based Systems},
volume = {348},
pages = {116346},
year = {2026},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2026.116346},
url = {https://www.sciencedirect.com/science/article/pii/S0950705126010725},
author = {Ling Wang and Qinchun Xu and Zezhong Li},
}
```

## 2026

| Year | Venue         | Paper Title and Paper Interpretation                                                       | Code                                      |
| :--- | :------------ | :----------------------------------------------------------------------------------------- | :---------------------------------------- |
| 2026 | *SBRC '26*    | Unsupervised DDoS Detection in High-Speed Networks: An Evaluation Using Real Transit Provider Data | [isp-ddos-auto-detector](https://github.com/MuriloChianfa/isp-ddos-auto-detector) |

## 2025

## 2024

## 2023

## 2021

## 2020

## 2019

## 2018

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