Merlion: A Machine Learning Library for Time Series (2109.09265v1)

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.

• The paper presents Merlion's unified framework that streamlines time series forecasting and anomaly detection through standardized data processing and evaluation.
• The paper details a layered architecture including data handling, modeling, post-processing, ensembling, and evaluation to facilitate robust benchmarking.
• The paper demonstrates significant performance gains in hyperparameter tuning and accuracy via extensive cross-dataset benchmarks on public and proprietary data.

Overview of Merlion: A Machine Learning Library for Time Series

This essay critically examines the "Merlion: A Machine Learning Library for Time Series" paper, which introduces an open-source library designed for time series analysis. Merlion aims to unify various models and datasets to facilitate anomaly detection and forecasting in both univariate and multivariate time series. Developed by Salesforce AI Research, Merlion presents a comprehensive solution for industry workflows and addresses several prevalent challenges in the field.

Key Features and Architecture

Merlion offers a standardized framework for data loading, pre-processing, benchmarking, visualization, AutoML, and model ensembling. The architecture comprises five layers:

1. Data Layer: Handles data loading, conversion to Merlion's TimeSeries format, and pre-processing tasks.
2. Modeling Layer: Supports a diverse array of models, including statistical methods and deep learning approaches, for forecasting and anomaly detection. It also incorporates AutoML for automated hyperparameter tuning.
3. Post-Processing Layer: Improves interpretability and reduces false positives in anomaly detection through calibration and thresholding.
4. Ensemble Layer: Provides model combination techniques and selection mechanisms to enhance robustness.
5. Evaluation Layer: Simulates live deployment scenarios and offers detailed evaluation metrics for model performance.

Models and Techniques

Merlion includes numerous models for univariate forecasting such as ARIMA, ETS, and deep learning methods. The multivariate forecasting models like Vector Autoregression and tree-based methods are adapted to accommodate arbitrary prediction horizons. For anomaly detection, Merlion supports both statistical approaches and deep learning models, ensuring comprehensive coverage for varied use cases.

Strong Numerical Results

The paper presents extensive benchmark results demonstrating Merlion's effectiveness across several datasets, including public datasets like M4 and internal datasets from Salesforce. AutoML features resulted in significant improvements in models like ARIMA and ETS, indicating the robustness of Merlion's hyperparameter optimization strategies.

Implications and Future Directions

Practically, Merlion serves as a one-stop solution for developing and benchmarking time series models, catering to both engineering and research needs. Theoretically, it advances understanding by integrating diverse methodologies within a single toolkit, thus facilitating comparative studies and further research.

Future work on Merlion includes expanding its support for new models, particularly deep learning and online learning algorithms. Additionally, developing a streaming platform is on the roadmap, which will further enhance its applicability in real-time production environments.

Merlion stands out by addressing many pain points in contemporary time series analysis workflows, providing a robust, unified framework that simplifies and improves the process of model development, deployment, and evaluation.