forecast_ensemble

forecast_ensemble combines multiple models into a single forecast and returns a combined package of models, their evaluated results including a composite score

Usage

The forecast is run using main.py with the following options

Request Requirements

The required input format is a JSON with the following fields:

Required:

  • data_filepath: filepath of the time series data to run the ensemble on
  • target_column: name of the input column
  • aggregate_operation: string operation to apply to target_column to create the time series
  • output_directory: directory to return the result JSON to

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for makani from gravatar.com makani

Credits

Author: Makani Cartwright

Requires

Python >=3.6

Classifiers

Report project as malware

Metadata

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for makani from gravatar.com makani

Credits

Author: Makani Cartwright

Requires

Python >=3.6

Classifiers

Report project as malware

Release files for amendment-forecast 1.5.12

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Source distribution (sdist)

Source distribution for amendment-forecast 1.5.12
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amendment_forecast-1.5.12.tar.gz 12.7 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for amendment-forecast 1.5.12
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amendment_forecast-1.5.12-py3-none-any.whl 13.5 kB Python 3 none any Details

Total release size: 26.3 kB

Release files / amendment_forecast-1.5.12.tar.gz

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