Metadata-Version: 2.1
Name: luminaire
Version: 0.1.0.dev6
Summary: Luminaire is a python package that provides ML driven solutions for monitoring time series data
Home-page: https://github.com/zillow/luminaire
Author: Zillow Group A.I. team
Author-email: luminaire-dev-oss@zillowgroup.com
License: Apache License 2.0
Project-URL: Documentation, https://github.com/zillow/luminaire/blob/master/README.md
Project-URL: Source, https://github.com/zillow/luminaire
Project-URL: Tracker, https://github.com/zillow/luminaire/issues
Description: # Luminaire: A hands-off Anomaly Detection Library
        
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        ![Luminaire Logo](luminaire_logo.png)
        
        **Table of contents**
        
        - [What is Luminaire](#what-is-luminaire)
        - [Quick Start](#quick-start)
        - [Anomaly Detection Flow](#anomaly-detection-flow)
        - [Contributing](#contributing)
        - [Acknowledgements](#acknowledgements)
        - [Development Team](#development-team)
        
        
        ## What is Luminaire
        
        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 in the data over time as well as uncontrollable variations.
        
        Please see the [full Luminaire documentation](luminaire/docs/_build/html/Introduction.html) 
        for detailed descriptions of the methods.
        
        ## Quick Start
        
        Luminaire can be installed from PyPI.
        
        ```bash
        pip install luminaire
        ```
        
        ## Time Series Outlier Detection Workflow
        ![Luminaire Flow](luminaire_flow.gif)
        
        Luminaire outlier detection workflow can be divided into 3 major components.
        
        ### Data Preprocessing and Profiling Component
        
        This component can be called for preparing a time series before training an anomaly detection model. This step applies
        all necessary fixes (missing data imputation, identifying and removing recent outliers from training data, necessary
        mathematical transformations, data truncation based on recent change points etc) and also generates profiling 
        information (hostorical change points, trend changes etc) for the training data. 
        
        The profiling information for time series data generates important information for an offline time series data and
        can be used to monitor irregular longer term swings or data drifts.
        
        ### Modeling Component
        
        This components 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](luminaire/docs/_build/html/basic_usage_tutorial/outlier_batch.html)
        for more information.
         
        Luminaire modeling step can be called after the data preprocessing and profiling step to perform necessary data preparation before training.
         
        ### Configuration Optimization Component
         
        Luminaire is integrated with configuration optimization capability for the hands-off anomaly detection approach where
        the user needs to provide almost no configuration for 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](luminaire/docs/_build/html/basic_usage_tutorial/optimization.html#fully-automatic-outlier-detection)
        for a detailed walkthrough. 
        
        ## Anomaly Detection for High Frequency Time Series
        
        Luminaire can monitor  a set of data points over windows instead of tracking individual. This approach becomes relevant
        for streaming use cases where individual fluctuations is not of a concern but the data is monitored for more sustained 
        fluctuations. See [anomaly detection for streaming data](/luminaire/docs/_build/html/basic_usage_tutorial/streaming.html)
        for detailed information.
        
        ## Contributing
        
        Want to help improve Luminaire? Check out our [contributing documentation](CONTRIBUTING.rst).
        
        ## Acknowledgements
        
        This project has leveraged methods described in the following scientific publications:
        
        1. 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.
        
        
        ## Development Team
        
        Luminaire is developped and maintained by [Sayan Chakraborty](https://github.com/sayanchk), [Smit Shah](https://github.com/shahsmit14), 
        [Kiumars Soltani](https://github.com/kiumarss), [Luyao Yang]( https://github.com/snazzyfox), [Anna Swigart](https://github.com/annaswigart), 
        [Kyle Buckingham](https://github.com/kylebuckingham) and many other contributors from the Zillow Group A.I. team.
        
Keywords: AnomalyDetection AutoML Batch Streaming
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6
Description-Content-Type: text/markdown
