Merlion: A Machine Learning Library for Time Series
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 seve...
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creator | Bhatnagar, Aadyot Kassianik, Paul Liu, Chenghao Tian Lan Yang, Wenzhuo Rowan Cassius Sahoo, Doyen Devansh Arpit Subramanian, Sri Woo, Gerald Saha, Amrita Jagota, Arun Kumar Gopalakrishnan, Gokulakrishnan Singh, Manpreet Krithika, K C Maddineni, Sukumar Cho, Daeki Zong, Bo Zhou, Yingbo Xiong, Caiming Savarese, Silvio Hoi, Steven Wang, Huan |
description | 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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subjects | Anomalies Benchmarks Datasets Libraries Machine learning Post-production processing Time series |
title | Merlion: A Machine Learning Library for Time Series |
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