scskde
Sequential Conditional Sampling from Kernel Density Estimates (SCS-KDE) is a method to fit non-parametric models to time series and generate synthetic realisations. The method implemented extends the original work in Sharma et al (1997) Streamflow simulation: A nonparametric approach [https://doi.org/10.1029/96WR02839] to include
- lag orders >= 1
- arbitrary seasonality
- vector-valued processes
- exogenous forcing
- arbitrary dependence structure
1 maintainer
Release files for scskde 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scskde-0.1.2.tar.gz | 60.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| scskde-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 68.3 kB
Release files / scskde-0.1.2.tar.gz
| Download URL | scskde-0.1.2.tar.gz |
|---|---|
| Size | 60.9 kB |
| Tags | Source |
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Release files / scskde-0.1.2-py3-none-any.whl
| Download URL | scskde-0.1.2-py3-none-any.whl |
|---|---|
| Size | 7.4 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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twine/6.1.0 CPython/3.11.6
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