torch-es
Double Seasonal Exponential Smoothing using PyTorch with batched data and multiple series training support.
📋 Roadmap
There are lots of tools built on top of the code in this repository, so the plan is to add them here eventually.
Here's what's published:
- 3d Holt-Winters implementation
- Additive and Multiplicative seasonalities
- Blender module to merge predictions from multiple series.
- Training loop for normal and bptt training.
- Uncertainty estimation via sampling.
- Additional losses
- RNN training on top of HW.
📚 Dependencies
- torch
- numpy
Metadata
Release files for torch-es 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| torch-es-0.0.1.tar.gz | 6.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| torch_es-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 16.9 kB
Release files / torch-es-0.0.1.tar.gz
| Download URL | torch-es-0.0.1.tar.gz |
|---|---|
| Size | 6.8 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.1
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Release files / torch_es-0.0.1-py3-none-any.whl
| Download URL | torch_es-0.0.1-py3-none-any.whl |
|---|---|
| Size | 10.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
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| Uploaded via |
twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.1
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