This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.

Related tags

Deep Learning python machine-learning time-series forecasting forecasting-models fbprophet forecasting-model streamlit-dashboard steamlit
Overview

📈 Automated Time Series Forecasting

Background: This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.

Try it out here: https://autoforecast-prophet.herokuapp.com/ (assuming I have not run out of heroku run time on the free tier!)

You'll be able to import your data from a CSV file, visualize trends and features, analyze forecast performance, and finally download the created forecast 😵

In beta mode

Created by Zach Renwick: https://twitter.com/zachrenwick.

Code available here: https://github.com/zachrenwick/streamlit_forecasting_app

Screenshot1 Screenshot2 Screenshot3 Screenshot4

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Comments
  • Bump streamlit from 1.9.0 to 1.11.1

    Bump streamlit from 1.9.0 to 1.11.1

    Bumps streamlit from 1.9.0 to 1.11.1.

    Commits

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    dependencies 
    opened by dependabot[bot] 0
  • Bump numpy from 1.18.4 to 1.21.0

    Bump numpy from 1.18.4 to 1.21.0

    Bumps numpy from 1.18.4 to 1.21.0.

    Release notes

    Sourced from numpy's releases.

    v1.21.0

    NumPy 1.21.0 Release Notes

    The NumPy 1.21.0 release highlights are

    • continued SIMD work covering more functions and platforms,
    • initial work on the new dtype infrastructure and casting,
    • universal2 wheels for Python 3.8 and Python 3.9 on Mac,
    • improved documentation,
    • improved annotations,
    • new PCG64DXSM bitgenerator for random numbers.

    In addition there are the usual large number of bug fixes and other improvements.

    The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

    :warning: Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

    • Optimization level -O3 results in many wrong warnings when running the tests.
    • On some hardware NumPy will hang in an infinite loop.

    New functions

    Add PCG64DXSM BitGenerator

    Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

    See upgrading-pcg64 for more details.

    (gh-18906)

    Expired deprecations

    • The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

    ... (truncated)

    Commits
    • b235f9e Merge pull request #19283 from charris/prepare-1.21.0-release
    • 34aebc2 MAINT: Update 1.21.0-notes.rst
    • 493b64b MAINT: Update 1.21.0-changelog.rst
    • 07d7e72 MAINT: Remove accidentally created directory.
    • 032fca5 Merge pull request #19280 from charris/backport-19277
    • 7d25b81 BUG: Fix refcount leak in ResultType
    • fa5754e BUG: Add missing DECREF in new path
    • 61127bb Merge pull request #19268 from charris/backport-19264
    • 143d45f Merge pull request #19269 from charris/backport-19228
    • d80e473 BUG: Removed typing for == and != in dtypes
    • Additional commits viewable in compare view

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Owner
Zach Renwick
Business Intelligence Developer. I do things and stuff with data.
Zach Renwick
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