Merlion: A Machine Learning Library for Time Series
This library aims to provide engineers and researchers a one-stop solution for developing and benchmarking time series models, but it is incremental as it builds on existing methods and tools.
The authors introduced Merlion, an open-source machine learning library for time series that provides a unified interface for models and datasets in anomaly detection and forecasting, along with features like AutoML and a deployment simulation framework, and reported benchmark numbers across baseline models.
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.