# Merlion: A Machine Learning Library for Time Series

arXiv: https://arxiv.org/abs/2109.09265 · HTML: https://mlsanity.com/paper/2109.09265/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://mlsanity.com/paper/1802.03903/) (2018)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [Temporal Pattern Attention for Multivariate Time Series Forecasting](https://mlsanity.com/paper/1809.04206/) (2018)
- [Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark](https://mlsanity.com/paper/1510.03336/) (2015)

## Built on by (most cited first, 25 of 25)

- [ETSformer: Exponential Smoothing Transformers for Time-series Forecasting](https://mlsanity.com/paper/2202.01381/) (2022)
- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [Darts: User-Friendly Modern Machine Learning for Time Series](https://mlsanity.com/paper/2110.03224/) (2021)
- [Improved Online Conformal Prediction via Strongly Adaptive Online Learning](https://mlsanity.com/paper/2302.07869/) (2023)
- [TAB: Unified Benchmarking of Time Series Anomaly Detection Methods](https://mlsanity.com/paper/2506.18046/) (2025)
- [Learning Fast and Slow for Online Time Series Forecasting](https://mlsanity.com/paper/2202.11672/) (2022)
- [ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons](https://mlsanity.com/paper/2310.07446/) (2023)
- [Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling](https://mlsanity.com/paper/2311.12550/) (2023)
- [PAC-Wrap: Semi-Supervised PAC Anomaly Detection](https://mlsanity.com/paper/2205.10798/) (2022)
- [Deep Contrastive One-Class Time Series Anomaly Detection](https://mlsanity.com/paper/2207.01472/) (2022)
- [MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection](https://mlsanity.com/paper/2205.11755/) (2022)
- [Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction](https://mlsanity.com/paper/2304.04697/) (2023)
- [A Novel Hyperdimensional Computing Framework for Online Time Series Forecasting on the Edge](https://mlsanity.com/paper/2402.01999/) (2024)
- [RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data](https://mlsanity.com/paper/2503.18385/) (2025)
- [Dynamic Combination of Heterogeneous Models for Hierarchical Time Series](https://mlsanity.com/paper/2112.11669/) (2021)
- [A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction](https://mlsanity.com/paper/2203.05757/) (2022)
- [LogAI: A Library for Log Analytics and Intelligence](https://mlsanity.com/paper/2301.13415/) (2023)
- [OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User](https://mlsanity.com/paper/2310.17748/) (2023)
- [ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection](https://mlsanity.com/paper/2604.13924/) (2026)
- [GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation](https://mlsanity.com/paper/2111.09314/) (2021)
- [Understanding Time Series Anomaly State Detection through One-Class Classification](https://mlsanity.com/paper/2402.02007/) (2024)
- [KnowIt: Deep Time Series Modeling and Interpretation](https://mlsanity.com/paper/2507.06009/) (2025)
- [An Improved Time Series Anomaly Detection by Applying Structural Similarity](https://mlsanity.com/paper/2509.20184/) (2025)
- [CARE: A Cascaded Framework for Efficient and Reliable Time Series Anomaly Detection](https://mlsanity.com/paper/2608.01885/) (2026)
- [MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detection](https://mlsanity.com/paper/2610.09473/) (2026)

## 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.