Package Health Score

79 / 100

security
No known security issues
popularity
Influential project
maintenance
Inactive
community
Active

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Popularity

Influential project

GitHub Stars: 18.49K
Forks: 4.53K
Contributors: 170

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package fbprophet, we found that it has been starred 18,486 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Sep 5, 2020
Direct Vulnerabilities: 0
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0
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0
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0
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Indirect Vulnerabilities: 0
C
0
H
0
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0
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License Risk: 0
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0
M
0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

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Maintenance

Inactive

Commit Frequency

Open Issues: 418
Open PR: 4
Last Release: 4 years ago
Last Commit: 28 days ago

Further analysis of the maintenance status of fbprophet based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for fbprophet is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 170
Funding: No

A good and healthy external contribution signal for fbprophet project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: >=3

Age: 8 years
Latest Release: 4 years ago
Dependencies: N/A
Versions: 13
Maintainers: 2
Wheels: No

Not sure how to use fbprophet?

To help you get started, we've collected the most common ways that fbprophet is being used within popular public projects.

EVEprosper / ProsperAPI / publicAPI / forecast_utils.py View on Github

Returns:
        pandas.DataFrame: collection of data + forecast info
            ['date', 'avgPrice', 'yhat', 'yhat_low', 'yhat_high', 'prediction']

    """
    data['date'] = pd.to_datetime(data['date'])
    filter_date = data['date'].max()

    ## Build DataFrame ##
    predict_df = pd.DataFrame()
    predict_df['ds'] = data['date']
    predict_df['y'] = data['avgPrice']

    ## Run prediction ##
    # https://facebookincubator.github.io/prophet/docs/quick_start.html#python-api
    model = Prophet()
    model.fit(predict_df)
    future = model.make_future_dataframe(periods=forecast_range)
    tst = model.predict(future)

    predict_df = pd.merge(
        predict_df, model.predict(future),
        on='ds',
        how='right'
    )

    ## Build report for endpoint ##
    report = pd.DataFrame()
    report['date'] = pd.to_datetime(predict_df['ds'], format='%Y-%m-%d')
    report['avgPrice'] = predict_df['y']
    report['yhat'] = predict_df['yhat']
    report['yhat_low'] = predict_df['yhat_lower']

View more ways to use fbprophet

Prophet: Automatic Forecasting Procedure

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

Prophet is open source software released by Facebook's Core Data Science team .

Full documentation and examples available at the homepage: https://facebook.github.io/prophet/

Important links

HTML documentation: https://facebook.github.io/prophet/docs/quick_start.html
Issue tracker: https://github.com/facebook/prophet/issues
Source code repository: https://github.com/facebook/prophet
Implementation of Prophet in R: https://cran.r-project.org/package=prophet

Other forecasting packages

Rob Hyndman's forecast package
Statsmodels

Installation

pip install fbprophet

Note: Installation requires PyStan, which has its own installation instructions. On Windows, PyStan requires a compiler so you'll need to follow the instructions. The key step is installing a recent C++ compiler

Installation using Docker and docker-compose (via Makefile)

Simply type make build and if everything is fine you should be able to make shell or alternative jump directly to make py-shell.

To run the tests, inside the container cd python/fbprophet and then python -m unittest

Example usage

  >>> from fbprophet import Prophet
  >>> m = Prophet()
  >>> m.fit(df)  # df is a pandas.DataFrame with 'y' and 'ds' columns
  >>> future = m.make_future_dataframe(periods=365)
  >>> m.predict(future)

FAQs

What is fbprophet?

Automatic Forecasting Procedure. Visit Snyk Advisor to see a full health score report for fbprophet, including popularity, security, maintenance & community analysis.

Is fbprophet popular?

The python package fbprophet receives a total of 226,490 weekly downloads. As such, fbprophet popularity was classified as an influential project. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is fbprophet well maintained?

We found indications that fbprophet is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is fbprophet safe to use?

The python package fbprophet was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 17 November-2024, at 03:11 (UTC).

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fbprophet

Automatic Forecasting Procedure For more information about how to use this package see README

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Not sure how to use fbprophet?

Prophet: Automatic Forecasting Procedure

Important links

Other forecasting packages

Installation

Installation using Docker and docker-compose (via Makefile)

Example usage

FAQs

What is fbprophet?

Is fbprophet popular?

Is fbprophet well maintained?

Is fbprophet safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Fix identified vulnerabilities

Monitor for new issues

Package Health Score

Explore Similar Packages

Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

Embed Package Health Score Badge

Keep your project healthy

Check your requirements.txt

Snyk Vulnerability Scanner

Not sure how to use fbprophet?

Prophet: Automatic Forecasting Procedure

Important links

Other forecasting packages

Installation

Installation using Docker and docker-compose (via Makefile)

Example usage

FAQs

What is fbprophet?

Is fbprophet popular?

Is fbprophet well maintained?

Is fbprophet safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

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