def owa(
target: np.ndarray,
forecast: np.ndarray,
past_data: np.ndarray,
seasonal_error: float,
start_date: pd.Timestamp,
) -> float:
r"""
.. math::
owa = 0.5*(smape/smape_naive + mase/mase_naive)
https://www.m4.unic.ac.cy/wp-content/uploads/2018/03/M4-Competitors-Guide.pdf
"""
# avoid import error due to circular dependency
from gluonts.model.baseline import naive_2
# calculate the forecast of the seasonal naive predictor
naive_median_fcst = naive_2(past_data,
len(target),
freq=start_date.freqstr)
owa = 0.5 * (
(Evaluator.smape(target, forecast) /
Evaluator.smape(target, naive_median_fcst)) +
(Evaluator.mase(target, forecast, seasonal_error) /
Evaluator.mase(target, naive_median_fcst, seasonal_error)))
return owa
def test_naive_2(prediction_length=FH, season_length=FRQ):
r_naive_2_inputs, r_naive_2_outputs = load_naive_2_data()
predictions = []
for i in range(len(r_naive_2_inputs)):
predictions.append(
naive_2(
r_naive_2_inputs[i],
prediction_length=prediction_length,
season_length=season_length,
))
predictions = np.array(predictions)
assert np.allclose(r_naive_2_outputs, predictions)