tune_hyperparameters_prophet.py

import itertools

param_grid = {  
    'changepoint_prior_scale': [0.001, 0.01, 0.1, 0.5],
    'seasonality_prior_scale': [0.01, 0.1, 1.0, 10.0],   
}

# Generate all combinations of parameters
all_params = [dict(zip(param_grid.keys(), v)) for v in itertools.product(*param_grid.values())]
maes = []  # Store the MAE for each params here
mapes = [] # Store the MAPE for each params here

# Use cross validation to evaluate all parameters
for params in all_params:
    m = Prophet(**params).fit(df_store_2_item_28)  # Fit model with given params
    df_cv = cross_validation(m, horizon='90 days', parallel="processes")
    df_p = performance_metrics(df_cv, rolling_window=1)
    maes.append(df_p['mae'].values[0])
    mapes.append(df_p['mape'].values[0])

# Find the best parameters
tuning_results = pd.DataFrame(all_params)
tuning_results['mae'] = maes
tuning_results['mape'] = mapes