Merlion: A Machine Learning Library for Time Series

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.

arXiv comment22 pages, 1 figure, 14 tables

References in corpus (3)

• Auto-Encoding Variational Bayes
• Sequence to Sequence Learning with Neural Networks
• Time-Series Anomaly Detection Service at Microsoft

Cited by in corpus (4)

• Darts: User-Friendly Modern Machine Learning for Time Series
• Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling
• OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User
• GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation