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Merlion: A Machine Learning Library for Time Series

20 September 2021
Aadyot Bhatnagar
Paul Kassianik
Chenghao Liu
Tian-Shing Lan
Wenzhuo Yang
Rowan Cassius
Doyen Sahoo
Devansh Arpit
Sri Subramanian
Gerald Woo
Amrita Saha
A. Jagota
Gokulakrishnan Gopalakrishnan
Manpreet Singh
K. C. Krithika
Sukumar Maddineni
Daeki Cho
Bo Zong
Yingbo Zhou
Caiming Xiong
Silvio Savarese
Guosheng Lin
Huan Wang
    AI4TS
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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.

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