airline_prophet.py

from fbprophet import Prophet

al_train_pp = al_train.to_timestamp(freq="M").reset_index()
# Prophet requires specific column names: ds and y
al_train_pp.columns = ["ds", "y"]

# turning on only yearly seasonality as this is monthly data.
# As the seasonality effects varies across years, we need multiplicative seasonality mode
m = Prophet(
    seasonality_mode="multiplicative",
    yearly_seasonality=True,
    weekly_seasonality=False,
    daily_seasonality=False,
)
m.fit(al_train_pp)

future = m.make_future_dataframe(periods=test_len, freq="M")
forecast = m.predict(future)
forecast = forecast.iloc[-test_len:]
forecast.rename(columns={"yhat_lower": "lower", "yhat_upper": "upper"}, inplace=True)
al_pph_mae, al_pph_mape = plot_forecast(
    al_train, al_test, forecast["yhat"], forecast[["lower", "upper"]]
)