lester-leon/fcvae

Code of FCVAE accepted by WWW 2024

FCVAE WWW 2024

Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
•A new CVAE structure that using frequency as a condition.
•Using global and local frequency information makes CVAE better reconstruct normal patterns.

Disclaimer

The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of FCVAE is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.

Get Started

1. Install Python=3.9.13, Pytorch=1.12.1, Pytorch_lightning=1.7.7, Numpy, Pandas
2. Train and evaluate.

python train.py --data_dir ./data/Yahoo  --window 48  --condition_emb_dim 64  --condition_mode 2  --save_file ./result  --gpu 0 --kernel_size 24 --stride 8 --dropout_rate 0.05

Parameter	Defination
data_dir	dataset address
window	size of window
condition_emb_dim	dimension of condition in CVAE
condition_mode	condition class(default 2)
save_file	address of save file
gpu	gpu number
kernel_size	size of small window in LFM
stride	stride in LFM when generating small windows
dropout_rate	dropout rate
use_label	1:supervised 0:unsupervised
latent_dim	dimension of latent space
max_epoch	training epoches
batch_size	batch_size
learning_rate	learning rate
data_pre_mode	datapreprocessing mode
missing_data_rate	missing data injection rate
mcmc_mode	default:2

Run All Results

/bin/bash run_all.sh