Merlion: A Machine Learning Library for Time Series
Abstract
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.
Cite
@article{arxiv.2109.09265,
title = {Merlion: A Machine Learning Library for Time Series},
author = {Aadyot Bhatnagar and Paul Kassianik and Chenghao Liu and Tian Lan and Wenzhuo Yang and Rowan Cassius and Doyen Sahoo and Devansh Arpit and Sri Subramanian and Gerald Woo and Amrita Saha and Arun Kumar Jagota and Gokulakrishnan Gopalakrishnan and Manpreet Singh and K C Krithika and Sukumar Maddineni and Daeki Cho and Bo Zong and Yingbo Zhou and Caiming Xiong and Silvio Savarese and Steven Hoi and Huan Wang},
journal= {arXiv preprint arXiv:2109.09265},
year = {2021}
}
Comments
22 pages, 1 figure, 14 tables