Open kdgutier opened 1 year ago
Concretely this block:
%%capture
if os.path.isfile('Y_hat.csv'):
Y_hat_df = pd.read_csv('Y_hat.csv')
Y_fitted_df = pd.read_csv('Y_fitted.csv')
Y_hat_df = Y_hat_df.set_index('unique_id')
Y_fitted_df = Y_fitted_df.set_index('unique_id')
else:
fcst = StatsForecast(
df=Y_train_df,
models=[AutoARIMA(season_length=12)],
fallback_model=[Naive()],
freq='M',
n_jobs=-1
)
Y_hat_df = fcst.forecast(h=12, fitted=True, level=[80])
Y_fitted_df = fcst.forecast_fitted_values()
Y_hat_df.to_csv('Y_hat.csv')
Y_fitted_df.to_csv('Y_fitted.csv')
In this nb: https://github.com/Nixtla/hierarchicalforecast/blob/main/nbs/examples/TourismLarge-Evaluation.ipynb
It would be convenient to have prefitted ARIMA base forecasts for medium and large datasets on S3.