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Luminaire is a python package that provides ML-driven solutions for monitoring time series data. Luminaire provides several anomaly detection and forecasting capabilities that incorporate correlational and seasonal patterns as well as uncontrollable variations in the data over time.
Install Luminaire from PyPI using
pip install luminaire
luminaire module in python
Check out Luminaire documentation for detailed description of methods and usage.
Luminaire outlier detection workflow can be divided into 3 major components:
This component can be called to prepare a time series prior to training an anomaly detection model on it. This step applies a number of methods that make anomaly detection more accurate and reliable, including missing data imputation, identifying and removing recent outliers from training data, necessary mathematical transformations, and data truncation based on recent change points. It also generates profiling information (historical change points, trend changes, etc.) that are considered in the training process.
Profiling information for time series data can be used to monitor data drift and irregular long-term swings.
This component performs time series model training based on the user-specified configuration OR optimized configuration (see Luminaire hyperparameter optimization). Luminaire model training is integrated with different structural time series models as well as filtering based models. See Luminaire outlier detection for more information.
The Luminaire modeling step can be called after the data preprocessing and profiling step to perform necessary data preparation before training.
Luminaire's integration with configuration optimization enables a hands-off anomaly detection process where the user needs to provide very minimal configuration for monitoring any type of time series data. This step can be combined with the preprocessing and modeling for any auto-configured anomaly detection use case. See fully automatic outlier detection for a detailed walkthrough.
Luminaire can also monitor a set of data points over windows of time instead of tracking individual data points. This approach is well-suited for streaming use cases where sustained fluctuations are of greater concern than individual fluctuations. See anomaly detection for streaming data for detailed information.
Want to help improve Luminaire? Check out our contributing documentation.
This project has leveraged methods described in the following scientific publications: