This repository provides the implementation of the Deep Autoencoder One-Class Time Series Anomaly Detection method, called AOC bellow.

The implementation uses the Merlion and the Tsaug libraries.

Time-series Anomaly Detection(AD) is widely used in monitoring and security pplications in various industries and has become a hot spot in the field of deep learning. Normalityrepresentation-based methods perform well in certain scenarios but may ignore some aspects of the overall normality. Feature-extraction-based methods always take a process of pre-training, whose target differs from AD, leading to a decline in AD performance. In this paper, we propose a new AD method called deep Autoencoding One-Class (AOC), which learns features with AutoEncoder(AE). Meanwhile, the normal context vectors from AE are constrained into a hypersphere small enough, similar to one-class methods. With an objective function that optimizes the two assumptions simultaneously, AOC learns various aspects of normality, which is more effective for AD. Experiments on public datasets show that our method outperforms existing baseline approaches.

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

git clone https://github.com/salesforce/Merlion.git
cd Merlion
pip install salesforce-merlion==1.1.1
pip install -e Merlion/ts_datasets/
pip install -r requirements.txt

The AOC repository already includes the merlion's data loading package ts_datasets. Please unzip the data/iops_competition/phase2.zip before running the program.

This directory contains experiment parameters for AOC model on IOpsCompetition, UCR datasets.

Source code of AOC models.

Directory where the experiment results and checkpoint are saved.

python aoc.py --selected_dataset UCR --device cuda --seed 2
python aoc.py --selected_dataset IOpsCompetition --device cuda --seed 2

This implementation is based on affiliation-metrics