This repository provides the implementation of the COCA+: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data method, called COCA+ below.

The accumulation of time-series signals and the absence of labels make time-series Anomaly Detection (AD) a self-supervised task of deep learning. Most methods based on normality assumptions face the following two challenges: (1) a single assumption could hardly characterize the whole normality or lead to some deviation, e.g. Contrastive Learning (CL) methods distance negative pairs, many of which consist of both normal samples, thus reducing the AD performance. (2) their basic assumption is that the training data is uncontaminated (free of anomalies), which is unrealistic in practice. When the proportion of contaminated data increases, the performance will be affected to varying degrees. This paper proposes a novel and robust approach based on multiple normality assumptions for time series anomaly detection with contaminated data. We fuse the assumptions of one-class classification and contrastive learning in a single learning process to characterize a more complete so-called normality. Meanwhile, we introduce the idea of outlier exposure in the latent space, which helps exclude the influence of abnormal samples while utilizing the contained anomaly knowledge. Boundaries become clearer by pushing potential anomalies away from clusters of normal samples. Extensive experiments on four real-world time-series datasets show the superior performance of the proposed method achieves state-of-the-art.

Link to our paper here. If you use this code for your research, please cite our paper:

We will add citation information later.

This code is based on Python 3.8, all requirements are written in requirements.txt. Additionally, we should install saleforce-merlion v1.1.1 and ts_dataset as Merlion suggested.

pip install salesforce-merlion==1.1.1
pip install -r requirements.txt

We acknowledge the contributors of the dataset, including AIOps, UCR, SWaT, and WADI. This repository already includes Merlion's data loading package ts_datasets.

1. AIOps Link: https://github.com/NetManAIOps/KPI-Anomaly-Detection
2. UCR Link: https://wu.renjie.im/research/anomaly-benchmarks-are-flawed/ and https://www.cs.ucr.edu/~eamonn/time_series_data_2018/UCR_TimeSeriesAnomalyDatasets2021.zip
3. Download and unzip the data in data/iops_competition and data/ucr respectively. e.g. For AIOps, download phase2.zip and unzip the data/iops_competition/phase2.zip before running the program.

1. For SWaT and WADI, you need to apply by their official tutorial. Link: https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/
2. Because multiple versions of these two datasets exist, we used their newer versions: SWaT.SWaT.A2_Dec2015, version 0 and WADI.A2_19Nov2019.
3. Download and unzip the data in data/swat and data/wadi respectively. Then run the swat_preprocessing() and wadi_preprocessing() functions in dataloader/data_preprocessing.py for preprocessing.

This directory contains experiment parameters for all models on AIOps (as IOpsCompetition in the code), UCR, SWaT, and WADI datasets.

Source code of CutAddPaste model.

Directory where the experiment result is saved.

# COCA+ Method (dataset_name: IOpsCompetition, UCR, SWaT, WADI)
python coca+.py --selected_dataset <dataset_name> --device cuda --seed 2

Anomaly Transformer(AnoTrans, AOT), AOC, RandomScore(RAS), NCAD, LSTMED, OC_SVM, IF, SR, RRCF, SVDD, DAMP, TS_AD(TCC)

We reiterate that in addition to our method, the source code of other baselines is based on the GitHub source code provided by their papers. For reproducibility, we changed the source code of their models as little as possible. We are grateful for the work on these papers.

We consult the GitHub source code of the paper corresponding to the baseline and then reproduce it. For baselines that use the same datasets as ours, we use their own recommended hyperparameters. For different datasets, we use the same hyperparameter optimization method Grid Search as our model to find the optimal hyperparameters.

Part of the code, especially the baseline code, is based on the following source code.

1. Anomaly Transformer(AOT)
2. AOC
3. Deep-SVDD-PyTorch
4. TS-TCC
5. DAMP and DAMP-python
6. LSTM_ED, SR, and IF are reproduced based on saleforce-merlion
7. RRCF
8. Metrics:affiliation-metrics