Convenience package for parallelized hyperparameter optimization (e.g. in Jupyter Notebooks) using grid search and CV
Project description
Time Series Hyperparameter Optimization (CV + Parallel)
Convenience package for optimizing hyperparameters for Time Series forecasting
using methods like ExponentialSmoothing or SARIMAX. Especially useful
for Jupyter Notebooks where parallelization (with e.g. ProcessPoolExecutor
)
only works when importing the function used in parallel.
Install it from PyPI
pip install ts-hyperparam-opt
Usage
from ts_hyperparam_opt import parallel_hyperparameter_optimization as pho
params_sarima = [
[(1,1,1), (1,1,1,7)],
[(1,1,0), (1,1,1,7)]
]
if __name__ == '__main__':
freeze_support()
results = process_map(functools.partial(pho.optimize_hyperparams,
data=df_data, func="sarima",
n_steps=15), params_sarima)
results_sorted = pho.sort_results(results)
Development
Alpha Version
Currently supported methods:
- (Triple) Exponential Smoothing (Holt-Winters)
- SARIMA(X)
Project details
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