Add extrapolate_season.predict() function

- the function implements a forecasting "method" similar to the
  seasonal naive method
  => instead of simply taking the last observation given a seasonal lag,
     it linearly extrapolates all observations of the same seasonal lag
     from the past into the future; conceptually, it is like the
     seasonal naive method with built-in smoothing
- the function is tested just like the `arima.predict()` and
  `ets.predict()` functions
  + rename the `tests.forecasts.methods.test_ts_methods` module
    into `tests.forecasts.methods.test_predictions`
- re-organize some constants in the `tests` package
- streamline some docstrings

tests/forecasts/conftest.py

@@ -9,13 +9,6 @@ from tests import config as test_config
 from urban_meal_delivery import config
 
 
-# See remarks in `vertical_datetime_index` fixture.
-VERTICAL_FREQUENCY = 7 * 12
-
-# The default `ns` suggested for the STL method.
-NS = 7
-
-
 @pytest.fixture
 def horizontal_datetime_index():
     """A `pd.Index` with `DateTime` values.