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Merlion: A Machine Learning Library for Time Series

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arxiv 2109.09265 v1 pith:H5TGM5EL submitted 2021-09-20 cs.LG cs.MSstat.ML

Aadyot Bhatnagar , Paul Kassianik , Chenghao Liu , Tian Lan , Wenzhuo Yang , Rowan Cassius , Doyen Sahoo , Devansh Arpit
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Sri Subramanian Gerald Woo Amrita Saha Arun Kumar Jagota Gokulakrishnan Gopalakrishnan Manpreet Singh K C Krithika Sukumar Maddineni Daeki Cho Bo Zong Yingbo Zhou Caiming Xiong Silvio Savarese Steven Hoi Huan Wang
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classification cs.LGcs.MSstat.ML
keywords seriestimemerlionlibrarymodelmodelsacrossanomaly
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. CARE: A Cascaded Framework for Efficient and Reliable Time Series Anomaly Detection

    cs.LG 2026-08 conditional novelty 6.0 of 10

    CARE accelerates time series anomaly detection by filtering out easy normal windows with a lightweight model, achieving 2.7x-4.8x speedup with maintained quality.

  2. ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection

    cs.LG 2026-04 conditional novelty 6.0 of 10

    Bicontinuous spinodal morphology preferentially illuminates the solid phase (⟨Φ⟩MC,3D/⟨Φ⟩MC,1D = 1.50–1.70), causing ~34% error in kinetic descriptors extracted with diffusion-approximation models.

  3. ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection

    cs.LG 2026-04 unverdicted novelty 5.0 of 10

    ASTER generates pseudo-anomalies in latent space to train a Transformer anomaly classifier with LLM-enriched representations, achieving state-of-the-art results on three benchmark datasets for unsupervised time-series...

  4. CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

    cs.LG 2025-09 conditional novelty 4.0 of 10

    CAPMix combines CutAddPaste anomaly injection, DTW-based label revision, and dual-space mixup to improve time-series anomaly detection, reporting gains over prior methods on five benchmarks.

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