Table of contents
- What is Luminaire
- Quick Start
- Time Series Outlier Detection Workflow
- Anomaly Detection for High Frequency Time Series
- Contributing
- Acknowledgements
- Development Team
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
pip install luminaireImport luminaire module in python
import luminaireCheck 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.
Please cite the following article if Luminaire is used for any research purpose or scientific publication:
Chakraborty, S., Shah, S., Soltani, K., Swigart, A., Yang, L., & Buckingham, K. (2020). Building an Automated and Self-Aware Anomaly Detection System. arXiv preprint arXiv:2011.05047. (arxiv link)
- Chakraborty, S., Shah, S., Soltani, K., & Swigart, A. (2019, December). Root Cause Detection Among Anomalous Time Series Using Temporal State Alignment. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) (pp. 523-528). IEEE. (arxiv link)
This project has leveraged methods described in the following scientific publications:
- Soule, Augustin, Kavé Salamatian, and Nina Taft. "Combining filtering and statistical methods for anomaly detection. " Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement. 2005.
Luminaire is developed and maintained by Sayan Chakraborty, Smit Shah, Kiumars Soltani, Luyao Yang, Anna Swigart, Kyle Buckingham and many other contributors from the Zillow Group A.I. team.