mlsanityData to 7 Oct 2026

Merlion: A Machine Learning Library for Time Series

arXiv 2109.09265 ↗First posted 20 Sep 2021cs.LG cs.MS stat.MLcited by 25

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Abstract (arXiv)

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.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 6 of its 41 references that are in the map. First posted on arXiv 20 Sep 2021.

Builds on

6 of 41 references.

References · 6

  1. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  2. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  3. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
  4. 2019Time-Series Anomaly Detection Service at Microsoftcited by 163
  5. 2018Temporal Pattern Attention for Multivariate Time Series Forecastingcited by 125
  6. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 87

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 6; 2023: 7; 2024: 2; 2025: 4; 2026: 3.

25 papers.

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  1. 2022ETSformer: Exponential Smoothing Transformers for Time-series Forecastingcited by 146
  2. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  3. 2021Darts: User-Friendly Modern Machine Learning for Time Seriescited by 90
  4. 2023Improved Online Conformal Prediction via Strongly Adaptive Online Learningcited by 76
  5. 2025TAB: Unified Benchmarking of Time Series Anomaly Detection Methodscited by 58
  6. 2022Learning Fast and Slow for Online Time Series Forecastingcited by 43
  7. 2023ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizonscited by 35
  8. 2023Explainable Time Series Anomaly Detection using Masked Latent Generative Modelingcited by 15
  9. 2022PAC-Wrap: Semi-Supervised PAC Anomaly Detectioncited by 10
  10. 2022Deep Contrastive One-Class Time Series Anomaly Detectioncited by 10
  11. 2022MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detectioncited by 5
  12. 2023Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Predictioncited by 5
13 more
  1. 2024A Novel Hyperdimensional Computing Framework for Online Time Series Forecasting on the Edgecited by 5
  2. 2025RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Datacited by 4
  3. 2021Dynamic Combination of Heterogeneous Models for Hierarchical Time Seriescited by 3
  4. 2022A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number predictioncited by 3
  5. 2023LogAI: A Library for Log Analytics and Intelligencecited by 2
  6. 2023OrionBench: Benchmarking Time Series Generative Models in the Service of the End-Usercited by 1
  7. 2026ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detectioncited by 1
  8. 2021GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimationcited by 0
  9. 2024Understanding Time Series Anomaly State Detection through One-Class Classificationcited by 0
  10. 2025KnowIt: Deep Time Series Modeling and Interpretationcited by 0
  11. 2025An Improved Time Series Anomaly Detection by Applying Structural Similaritycited by 0
  12. 2026CARE: A Cascaded Framework for Efficient and Reliable Time Series Anomaly Detectioncited by 0
  13. 2026MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detectioncited by 0