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[ICDE'2024] TFMAE

Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection

This is a official PyTorch implementation of the paper: Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection.

The most fundamental challenge for time series anomaly detection is to to identify observations that differ significantly from the remaining observations. Reconstruction-based methods still dominate, but the representation learning with anomalies bias and distribution shifts might hurt the performance. In this paper, we propose temporal-frequency masked autoencoders (TFMAE), a unsupervised time series anomaly detection model.

  • Overall: Overall architecture of the proposed TFMAE. The left part contains temporal-frequency masks, the middle part is transformer-based autoencoders, and the right part shows our objective functions. $D_{KL}$ means the Kullback–Leibler divergence.
Figure1
Figure 1. The workflow of the TFMAE framework.

📖 Main Result

We compare our model with 14 baselines based on various metrics. Extensive experiments show that TFMAE achieves the best performance on five benchmark datasets compared to state-of-the-arts.

Figure2
Table 1. Overall results on real-world datasets.
Figure3
Figure 2. Performance metrics are compared on the SMD dataset, with F1-Score represented in $%$ on the $y$-axis, speed on the $x$-axis, and memory footprint indicated by the size of the circles for each method.

📂 Code Description

There are nine files/folders in the source.

  • main.py: The main python file.
  • solver.py: Another python file. The training, validation, and testing processing are all in there.
  • config: Configuration folder. You can adjustment all parameters in there.
  • cpt: Model weights folder. You can test performance through trained weight files.
  • data_factory: The preprocessing folder/file. All datasets preprocessing codes are here.
  • dataset: The dataset folder, and you can unzip these files.
  • model: TFMAE model folder. The details can be corresponding to paper’s Section Section IV.
  • pics: Images needed in readme.md.
  • paper: Paper in PDF.
  • result: In our code demo, we can automatically save the results and hyper-parameters in this folder.

⚡ Get Start

  1. Install Python 3.x, PyTorch >= 1.12.
  2. Unzip data in the dataset folder. All the datasets are well pre-processed.
  3. Train and evaluate. You can reproduce the experiment results as follows:
python main.py --config config/MSL.conf
python main.py --config config/PSM.conf
python main.py --config config/SMD.conf
python main.py --config config/SMAP.conf
python main.py --config config/SWaT.conf

💬 Citation

@inproceedings{fang2024temporal,
  title={Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection},
  author={Fang, Yuchen and Xie, Jiandong and Zhao, Yan and Chen, Lu and Gao, Yunjun and Zheng, Kai},
  booktitle={2024 IEEE 40th International Conference on Data Engineering (ICDE)},
  pages={1228--1241},
  year={2024},
  organization={IEEE}
}