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.
翻译:我们引入了Merlion(一个开放源码机器学习库,用于时间序列),它为许多常用模型和数据集提供了一个统一的界面,用于在单亚氏和多变时间序列以及标准预处理/后处理层上探测和预测异常现象,并使用标准前/后处理层进行异常现象检测和预测,它有几个模块来改进使用便利,包括可视化、异常分数校准以改进互换性、超参数调和模型选择的自动ML(Automle)和模型组合。Merlion还提供了一个独特的评估框架,模拟生产模型的现场部署和再培训。该图书馆旨在为工程师和研究人员提供一站式解决方案,以快速开发用于其具体时间序列需求的模型,并在多个时间序列数据集中对其进行基准测量。在本技术报告中,我们突出Merlion的结构和主要功能,并报告不同基线模型和组合的基准数字。