Toolset for time series forecasting, based on fundamental data science libraries like Pandas, statsmodels, sklearn etc. Supports model design as well as storing and accessing forecasts. It contains functionality that is both useful for data & model exploration as well as integrating into production code.
Metadata
Release files for ts-forecasting-pipeline 0.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ts_forecasting_pipeline-0.4.0.tar.gz | 13.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ts_forecasting_pipeline-0.4.0-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details |
Total release size: 30.5 kB
Release files / ts_forecasting_pipeline-0.4.0.tar.gz
| Download URL | ts_forecasting_pipeline-0.4.0.tar.gz |
|---|---|
| Size | 13.3 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.11.0 pkginfo/1.4.2 requests/2.19.1 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/3.6.6
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Release files / ts_forecasting_pipeline-0.4.0-py2.py3-none-any.whl
| Download URL | ts_forecasting_pipeline-0.4.0-py2.py3-none-any.whl |
|---|---|
| Size | 17.2 kB |
| Tags | Python 2 Python 3 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.11.0 pkginfo/1.4.2 requests/2.19.1 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/3.6.6
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