PyTorch implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Related tags

Deep Learning deep-learning transformers anomaly-detection
Overview

Anomaly Transformer in PyTorch

This is an implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. This paper has been accepted as a Spotlight Paper at ICLR 2022.

Repository currently a work in progress.

Usage

Requirements

Install dependences into a virtualenv:

$ python -m venv env
$ source env/bin/activate
(env) $ pip install -r requirements.txt

Written with python version 3.8.11

Data and Configuration

Custom datasets can be placed in the data/ dir. Edits should be made to the conf/data/default.yaml file to reflect the correct properties of the data. All other configuration hyperparameters can be set in the hydra configs.

Train

Once properly configured, a model can be trained via python train.py.

Citations

@misc{xu2021anomaly,
      title={Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy},
      author={Jiehui Xu and Haixu Wu and Jianmin Wang and Mingsheng Long},
      year={2021},
      eprint={2110.02642},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
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Comments
  • update model.py

    update model.py

    Hi, thanks for sharing codes. I tried to revise the model according to your paper. 1). Fixed KL divergency calculation bug. 2). Added feature for supporting batch processing. (input: [batch, N, d]) 3). Fixed problem when the dimension of input is not equal to that of Transformer. ( d != d_model)

    opened by hyli666 0
  • Hi, I have a question !

    Hi, I have a question !

    Your research is very impressive and wonderful. However, I have one question while reading the paper.

    in this paper, For the maximize phase, we optimize the series-association to enlarge the association discrepancy. This process forces the series-association to pay more attention to the non-adjacent horizon.

    Maximize phases seem to focus more on series-association on adjacent horizon, but why is this non-adjacent horizon?

    In my opinion, if the sigma of the prior association is much less than 1, the series association will only look at more adjacent areas.

    Thank you image

    opened by Yoontae6719 0
  • Incorrect prior_association() ?

    Incorrect prior_association() ?

    It seems the method prior_association() can not back propagate gradient to train Ws, or maybe I misunderstood? https://github.com/spencerbraun/anomaly_transformer_pytorch/blob/6d15200911260eee910a3664d70f07886c47708b/model.py#L41-L45

    according to paper, is this the right way? gaussian = 1 / math.sqrt(2 * math.pi) / self.sigma * torch.exp(- 0.5 * (p / self.sigma).pow(2))

    opened by sappersapper 4
  • Loss for minimize and maximize phase

    Loss for minimize and maximize phase

    There is an issue with the loss: https://github.com/spencerbraun/anomaly_transformer_pytorch/blob/6d15200911260eee910a3664d70f07886c47708b/train.py#L53 the reconstruction loss cancels, and we only end up with -2*discrepancy loss. Additionally, the stop-gradient as described in the paper is not implemented.

    opened by raphaelreinauer 7
Owner
spencerbraun
NLP Researcher @PrimerAI.
spencerbraun
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