  We introduce Merlion, an open-source machine learning library for time
series. It features a unified interface for many commonly used models and
datasets for anomaly detection and forecasting on both univariate and
multivariate time series, along with standard pre/post-processing layers. It
has several modules to improve ease-of-use, including visualization, anomaly
score calibration to improve interpetability, AutoML for hyperparameter tuning
and model selection, and model ensembling. Merlion also provides a unique
evaluation framework that simulates the live deployment and re-training of a
model in production. This library aims to provide engineers and researchers a
one-stop solution to rapidly develop models for their specific time series
needs and benchmark them across multiple time series datasets. In this
technical report, we highlight Merlion's architecture and major
functionalities, and we report benchmark numbers across different baseline
models and ensembles.
