This file outlines how you should navigate the Jupyter notebooks in this folder. All new users should start with TimeSeries.ipynb, which explains how to use Merlion's UnivariateTimeSeries and TimeSeries classes. These classes are the core data format used throughout the repo.

If you are interested in anomaly detection, you should next read anomaly/AnomalyIntro.ipynb to understand how to use anomaly detection models in Merlion. Afterwards, if you want to implement a new anomaly detection model in Merlion, please read CONTRIBUTING.md and anomaly/AnomalyNewModel.ipynb.

If you are interested in forecasting, you should next read forecast/ForecastIntro.ipynb to understand how to use forecasting models in Merlion. Afterward, if you want to implement a new forecasting model in Merlion, please read CONTRIBUTING.md and and forecast/ForecastNewModel.ipynb.

We offer more advanced tutorials on specific high-performing models (AutoSARIMA and Mixture of Experts forecaster) in the advanced subdirectory. If you are interested in other utilities offered by the merlion package, look at the resources inside the misc subdirectory. For example, misc/generate_synthetic_tsad_dataset.py is a script for generating an artifical anomaly detection dataset using merlion's time series generation and anomaly injection modules. This particular dataset may be loaded using the data loader ts_datasets.anomaly.Synthetic.