https://github.com/ralgond/tsad-ucr

Time Series Anomaly Detection Solutions for KDD2021 UCR dataset
https://github.com/ralgond/tsad-ucr

anomaly-detection autoencoder data-augmentation deep-learning kdd2021 keras matrix-profile pytorch tensorflow time-series ucr variational-autoencoder

Last synced: 3 days ago
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Time Series Anomaly Detection Solutions for KDD2021 UCR dataset

• Host: GitHub
• URL: https://github.com/ralgond/tsad-ucr
• Owner: ralgond
• License: apache-2.0
• Created: 2022-10-07T00:29:58.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-11-15T09:40:07.000Z (almost 2 years ago)
• Last Synced: 2023-07-03T08:59:33.006Z (over 1 year ago)
• Topics: anomaly-detection, autoencoder, data-augmentation, deep-learning, kdd2021, keras, matrix-profile, pytorch, tensorflow, time-series, ucr, variational-autoencoder
• Language: Python
• Homepage:
• Size: 134 KB
• Stars: 6
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# TSAD-UCR

Time Series Anomaly Detection Solutions for KDD2021 UCR dataset

## The rule

https://compete.hexagon-ml.com/practice/competition/39/

## How to run

1. Download ucr data from [here](https://github.com/ralgond/KDD2021-UCR), and create a directory named ".data" in this repo directory,

unzip the downloaded data to ".data".

2. Enter a directory, such as Augment, install package according to "requirements.txt", and execute "python main.py".

## 得分

|solution name|score|directory|resources|

|-------------|-----|---------|---------|

| genta       | 83.6|genta    |[Video](https://www.youtube.com/watch?v=J_Ebbql9jCo)|

| Augment (3way) | 73.2 | Augment | 参考[Paper](https://arxiv.org/pdf/1812.04606.pdf)|

| DeepSAD  | 64.8 | DeepSAD | 参考[Code](https://github.com/lukasruff/Deep-SAD-PyTorch)|

| DeepSVDD  | 54.4 | DeepSVDD | 参考[Code](https://github.com/lukasruff/Deep-SVDD-PyTorch)|

| matrix-profile-1| 52.8| matrixprofile-1| 参考[Page](https://www.cs.ucr.edu/~eamonn/MatrixProfile.html) |

| LOF | 42.8 | LOF | 参考[Doc](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html) |

| vanilla_ae  | 29.2| ae-torch/ae_conv.py ||

| vanilla_vae | 27.6| ae-tf/vae.py | 参考[Doc](https://keras.io/examples/generative/vae/)|

| Isolation Forest | 23.6 | IF| 参考[Doc](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) |