spcqe
Smooth (multi-) periodic consistent quantile estimation. We attempt to follow the sklearn "fit/transform" API, and the main class inherets TransformerMixin and BaseEstimator from sklearn.base.
Installation
The package is available on both PyPI and conda-forge.
pip installation:
pip install spcqe
conda installation:
conda install conda-forge::spcqe
You may also clone the repository to your local machine and install with pip by navigating to the project directory and running:
pip install .
If working on the files in this package (i.e. fixing bugs or adding features), it useful to install in editable mode:
pip install -e .
Usage
from spcqe.quantiles import SmoothPeriodicQuantiles
y1 = ... # some data with hourly measurement exhibiting daily, weekly, and yearly periodicities
P1 = int(365*24)
P2 = int(7*24)
P3 = int(24)
K = 3
l = 0.1
spq = SmoothPeriodicQuantiles(K, [P1, P2, P3], weight=l)
spq.fit(y1)
Examples
Many examples Jupyter notebooks are available in the notebooks folder.
Acknowledgement
This material is based upon work supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technologies Office Award Number 38529, "PVInsight".
1 maintainer
Metadata
1 maintainer
Release files for spcqe 0.3.0
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Total release size: 80.3 MB
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