evaluate_forecast.py

from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_percentage_error


def plot_forecast(series_train, series_test, forecast, forecast_int=None):

    mae = mean_absolute_error(series_test, forecast)
    mape = mean_absolute_percentage_error(series_test, forecast)

    plt.figure(figsize=(12, 6))
    plt.title(f"MAE: {mae:.2f}, MAPE: {mape:.3f}", size=18)
    series_train.plot(label="train", color="b")
    series_test.plot(label="test", color="g")
    forecast.index = series_test.index
    forecast.plot(label="forecast", color="r")
    if forecast_int is not None:
        plt.fill_between(
            series_test.index,
            forecast_int["lower"],
            forecast_int["upper"],
            alpha=0.2,
            color="dimgray",
        )
    plt.legend(prop={"size": 16})
    plt.show()

    return mae, mape

fh = np.arange(test_len) + 1
forecast = forecaster.predict(fh=fh)
coverage = 0.9 # confidence interval
forecast_int = forecaster.predict_interval(fh=fh, coverage=coverage)['Coverage'][coverage]
sun_arima_mae, sun_arima_mape = plot_forecast(
    sun_train, sun_test, forecast, forecast_int
)