InterFusion

KDD 2021: Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding

InterFusion is an unsupervised MTS anomaly detection and interpretation method. It's core idea is to model the normal patterns of MTS using HVAE with jointly trained hierarchical stochastic latent variables, each of which explicitly learns low-dimensional inter-metric or temporal embeddings. You may refer to our paper for more details.

Getting Started

Clone the repo

git clone https://github.com/zhhlee/InterFusion.git && cd InterFusion

Get data

The datasets used in this paper are in folder data. You may refer to data/Dataset Description for more details.

Install dependencies (with python 3.6+)

(virtualenv is recommended)

pip install -r requirements.txt

The code is tested under the following basic environments:

OS: Ubuntu 18.04
GPU: GTX 1080 Ti
Cuda: 9.0.176
Python: 3.6.6

Run the code

Please set the root directory of the project as your Python path.

For dataset ASD and SMD:

python algorithm/stack_train.py --dataset=omi-1			# training
python algorithm/stack_predict.py --load_model_dir=./results/stack_train/	# evaluation

For dataset SWaT and WADI (Note: you need to acquire these datasets first following data/Dataset Description and explib/raw_data_converter):

SWaT:

python algorithm/stack_train.py --dataset=SWaT --train.train_start=21600 --train.valid_portion=0.1 --model.window_length=30 '--model.output_shape=[15, 15, 30]' --model.z2_dim=8	# training
python algorithm/stack_predict.py --load_model_dir=./results/stack_train/ --mcmc_track=False	# evaluation

WADI:

python algorithm/stack_train.py --dataset=WADI --train.train_start=259200 --train.max_train_size=789371 --train.valid_portion=0.1 --model.window_length=30 '--model.output_shape=[15, 15, 30]' --model.z2_dim=8 # training
python algorithm/stack_predict.py --load_model_dir=./results/stack_train/ --mcmc_track=False	# evaluation

The default model configurations are in algorithm/InterFusion.py, train configs in algorithm/stack_train.py, and evaluation configs in algorithm/stack_predict.py. You may overwrite the configs using command line args. For example:

python algorithm/stack_train.py --dataset=omi-1 --model.z_dim=5 --train.batch_size=128
python algorithm/stack_predict.py --load_model_dir=./results/stack_train/ --test_batch_size=100

Run on your own dataset

1. Put your train/test/label files under data/processed folder. e.g., ds_train.pkl, ds_test.pkl, ds_test_label.pkl with shape (train_length, feature_dim), (test_length, feature_dim), (test_length,), respectively.
2. Put the interpretation files (optional) under data/interpretation_label folder.
3. Edit get_data_dim in algorithm/utils.py to add your dataset info.
4. Run the code following the instructions above.

Results

After running the algorithm, the results are shown in the results folder. The main results are:

Model: results/stack_train/result_params/
Training config: results/stack_train/config.json
Testing config: results/stack_predict/config.json
Testing statistics: results/stack_predict/result.json

If you find this code useful for your research, please cite our paper:

@inproceedings{li2021multivariate,
  title={Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding},
  author={Li, Zhihan and Zhao, Youjian and Han, Jiaqi and Su, Ya and Jiao, Rui and Wen, Xidao and Pei, Dan},
  booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
  pages={3220--3230},
  year={2021}
}