Metadata-Version: 1.2
Name: sam_anomaly_detector
Version: 2.1
Summary: Sam media anomaly detector library
Home-page: https://pypi.python.org/pypi/sam-anomaly-detector
Author: Hossein Jazayeri
Author-email: hossein@sam-media.com
License: Apache-2.0
Description-Content-Type: UNKNOWN
Description: Time series forecasting and anomaly detection library on top of fbprophet
        
        .. code:: python
            import pandas as pd
            from psycopg2 import connect
            from sam_anomaly_detector import Forecaster
            df_data = pd.read_csv('dataset.csv', columns=['ds', 'y'])
            json_data = df_data.to_json(orient='records')
            anomalies = Detector().forecast_today(dataset=json_data)
            print(anomalies)
        
        
        - Input data should be a panda DataFrame having time and aggregated data
        - Passed columns to forecaster should be 'ds' for 'time' and 'y' for 'aggregated data'
        - Output is a panda DataFrame of anomalies. Important columns are:
            - actual: today's actual value
            - yhat_lower: forecast lower boundary
            - yhat: : forecastted value
            - yhat_upper: forecast upper boundary
            - std: standard diviation from boundaries. negative value means how far it is from 'yhat_lower',
                     positive value means how far it is from 'yhat_upper'
        
Keywords: forecast,fbprophet,anomaly-detection
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3
