Welcome to Merlion’s documentation!

Merlion is a Python library for time series intelligence. It features a unified interface for many commonly used models and datasets for forecasting, anomaly detection, and change point detection on both univariate and multivariate time series, along with standard pre-processing and 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.

Installation

Merlion consists of two sub-packages: merlion implements the library’s core time series intelligence features, and ts_datasets provides standardized data loaders for multiple time series datasets. These loaders load time series as pandas.DataFrame s with accompanying metadata.

You can install merlion from PyPI by calling pip install salesforce-merlion. You may install from source by cloning the Merlion repo and calling pip install Merlion/, or pip install -e Merlion/ to install in editable mode. You may install additional optional dependencies via pip install salesforce-merlion[all], or by calling pip install "Merlion/[all]" if installing from source. Individually, the optional dependencies include plot for interactive plots and deep-learning for all deep learning models.

To install the data loading package ts_datasets, clone the Merlion repo and call pip install -e Merlion/ts_datasets/. This package must be installed in editable mode (i.e. with the -e flag) if you don’t want to manually specify the root directory of every dataset when initializing its data loader.

Note the following external dependencies:

1. Some of our forecasting models depend on OpenMP. Some of our forecasting models depend on OpenMP. If using conda, please conda install -c conda-forge lightgbm before installing our package. This will ensure that OpenMP is configured to work with the lightgbm package (one of our dependencies) in your conda environment. If using Mac, please install Homebrew and call brew install libomp so that the OpenMP libary is available for the model. This is relevant for the LGBMForecaster, which is also used as a part of the DefaultForecaster.

2. Some of our anomaly detection models depend on having the Java Development Kit (JDK) installed. For Ubuntu, call sudo apt-get install openjdk-11-jdk. For Mac OS, install Homebrew and call brew tap adoptopenjdk/openjdk && brew install --cask adoptopenjdk11. Also ensure that java can be found on your PATH, and that the JAVA_HOME environment variable is set. This is relevant for the RandomCutForest which is also used as a part of the DefaultDetector.

Getting Started

To get started, we recommend the linked tutorials on anomaly detection and forecasting. After that, you should read in more detail about Merlion’s main data structure for representing time series here.

Contents:

• merlion: Time Series Intelligence
  • Subpackages
  • Submodules
  • merlion.plot module
• ts_datasets: Easy Data Loading
  • Subpackages
  • Submodules
  • datasets.base module
• Tutorials & Example Code
  • Basics
  • Anomaly Detection
  • Forecasting
  • Advanced Features

Indices and tables

• Index

• Module Index

• Search Page