1. AAAAI 2023 1.5-hour Lab based tutorial
2. MLSys 2022 half day tutorial
3. KDD 2022 3-hour tutorial
4. ICDE 2022 tutorial
5. DASFAA 2022 tutorial

• IBM Developer API Hub

• UI

1. Python 3
2. Credentials to access the API service (Please follow the instructions or tutorial)
3. Clone the repository

git clone /~https://github.com/IBM/anomaly-detection-code-pattern.git
cd anomaly-detection-code-pattern/

4. (Optional) Create a virtual environment

virtualenv ad_env
source ad_env/bin/activate

5. Install required packages

pip install -r requirements.txt

6. Open Jupyter notebook in current directory

python -m ipykernel install --user --name=ad_env  # optional: add virtual environment to jupyter notebook
jupyter notebook

Here are the list of provided notebooks:

1. Univariate_AD_service_public_data.ipynb: Anomaly detection on univariate public data
2. Univariate_AD_service_sample_data.ipynb: Anomaly detection on univariate sample data
3. Multivariate_AD_service_sample_data.ipynb: Anomaly detection on multivariate sample data
4. Regression-aware_AD_service_sample_data.ipynb: Regression based anomaly detection
5. MixtureModel-aware_AD_service_sample_data.ipynb: Mixture model based anomaly detection

1. API Service in IBM API Hub
2. API Service in IBM Learning Path