Automatically build deep learning and ARIMA models and use them for an ensemble of models. Library has different tools for time series analysis and has a simple architecture to use.
1 maintainer
Release files for TSEnsemble 0.1.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| TSEnsemble-0.1.3.tar.gz | 23.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| TSEnsemble-0.1.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 48.2 kB
Release files / TSEnsemble-0.1.3.tar.gz
| Download URL | TSEnsemble-0.1.3.tar.gz |
|---|---|
| Size | 23.5 kB |
| Tags | Source |
|
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/4.0.2 CPython/3.11.0
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Release files / TSEnsemble-0.1.3-py3-none-any.whl
| Download URL | TSEnsemble-0.1.3-py3-none-any.whl |
|---|---|
| Size | 24.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
288a5dc38e9acfc67dc7c40dce0228786bb520fc9f171d81f7cd8de48da7ab54
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.0
|
