Skip to main content

Sam media anomaly detector library

Project 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'


Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sam_anomaly_detector-1.3.tar.gz (7.8 kB view hashes)

Uploaded Source

Built Distribution

sam_anomaly_detector-1.3-py3-none-any.whl (5.7 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page