Official repository for the paper "When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection", AAAI 2024. [Arxiv]
This repository is not yet completed, so please check this as a reference only.
Time-series Anomaly Detection (TSAD) deals with the problem of detecting anomalous timesteps, by learning "normalities" from the sequence of observations.
Our research starts from the question:
"Is the concept of normal remain consistent over time?"
In time-series anomaly detection task, we challenge this assumption, and show that the concept of normal changes through time.
We call this problem as the "New Normal Problem," with the explicit aim of advocating the need to learn how to adapt to emerging norms rather than clinging to historical ones.
Our research also highlights the efficacy of our simple adaptation and normalization strategy, emphasizing the importance of adapting rather than persisting in the past.
All datasets are assumed to be in "data" folder.
- NeurIPS-TS NeurIPS-TS dataset are created using the principles in https://openreview.net/forum?id=r8IvOsnHchr. We prepared Univariate/Multivariate dataset, for each data length being 1000. For data generation, please refer to univariate_generator, multivariate_generator.
SWaT and WADI dataset has two types of data: train (normal) and test (abnormal). Train set does not contain anomaly set. Test set has anomalies driven by researcher's attack scenarios. Request via guidelines in the link.
- SWaT (2022-10-25) : Secure Water Treatment Dataset
- With shared google drive link after request, refer to SWaT.A1 & A2_Dec 2015
- For Normal Dataset, refer to ./Physical/SWaT_Dataset_Normal_v0.xlsx
- For Attack Dataset, refer to ./Physical/SWaT_Dataset_Attack_v0.xlsx
- convert xlsx using read_xlsx_and_convert_to_csv in utils/tools.py
- WADI (2022-10-25) : Water Distribution Dataset
- With shared google drive link after request, refer to WADI.A2_19 Nov 2019
- For Normal Dataset, refer to ./WADI_14days_new.csv
- For Attack Dataset, refer to ./WADI_attackdataLABLE.csv
- SMD: Server Machine Dataset
- Download: https://github.com/NetManAIOps/OmniAnomaly/ServerMachineDataset directory
- MSL and SMAP
- Download with instructions from: https://github.com/khundman/telemanom
curl -O https://s3-us-west-2.amazonaws.com/telemanom/data.zip && mv data.zip data/NASA.zip && unzip data/NASA.zip -d data/NASA && rm data/NASA.zip- CreditCard
- https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
- As a reference, take a look at: https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html
- Yahoo
- request to: https://webscope.sandbox.yahoo.com/
set threshold to offline best f1 score
python test.py (...) infer_options=["offline"] thresholding=off_f1_best
save run information
plot_anomaly_scores: False
plot_recon_status: False
save_result: True # save result in pandas dataframe
load_anoscs: True # load previously calcuated anomaly score (if not exist, start from scratch.)
save_outputs: False # gt (X.pt) and reconstructed (Xhat.pt)
save_roc_curve: False # whether to save fpr, tpr, thrs from sklearn.roc_curve
- Refactoring
- Anoshift Benchmark