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/methods/test_predictions.py

@@ -0,0 +1,130 @@
+"""Test all the `*.predict()` functions in the `methods` sub-package."""
+
+import datetime as dt
+
+import pandas as pd
+import pytest
+
+from tests import config as test_config
+from urban_meal_delivery import config
+from urban_meal_delivery.forecasts.methods import arima
+from urban_meal_delivery.forecasts.methods import ets
+from urban_meal_delivery.forecasts.methods import extrapolate_season
+
+
+@pytest.fixture
+def forecast_interval():
+    """A `pd.Index` with `DateTime` values ...
+
+    ... that takes place one day after the `START`-`END` horizon and
+    resembles an entire day (`12` "start_at" values as we use `LONG_TIME_STEP`).
+    """
+    future_day = test_config.END.date() + dt.timedelta(days=1)
+    first_start_at = dt.datetime(
+        future_day.year, future_day.month, future_day.day, config.SERVICE_START, 0,
+    )
+    end_of_day = dt.datetime(
+        future_day.year, future_day.month, future_day.day, config.SERVICE_END, 0,
+    )
+
+    gen = (
+        start_at
+        for start_at in pd.date_range(
+            first_start_at, end_of_day, freq=f'{test_config.LONG_TIME_STEP}T',
+        )
+        if config.SERVICE_START <= start_at.hour < config.SERVICE_END
+    )
+
+    index = pd.Index(gen)
+    index.name = 'start_at'
+
+    return index
+
+
+@pytest.fixture
+def forecast_time_step():
+    """A `pd.Index` with one `DateTime` value, resembling `NOON`."""
+    future_day = test_config.END.date() + dt.timedelta(days=1)
+
+    start_at = dt.datetime(
+        future_day.year, future_day.month, future_day.day, test_config.NOON, 0,
+    )
+
+    index = pd.Index([start_at])
+    index.name = 'start_at'
+
+    return index
+
+
+@pytest.mark.r
+@pytest.mark.parametrize(
+    'func', [arima.predict, ets.predict, extrapolate_season.predict],
+)
+class TestMakePredictions:
+    """Make predictions with `arima.predict()` and `ets.predict()`."""
+
+    def test_training_data_contains_nan_values(
+        self, func, vertical_no_demand, forecast_interval,
+    ):
+        """`training_ts` must not contain `NaN` values."""
+        vertical_no_demand.iloc[0] = pd.NA
+
+        with pytest.raises(ValueError, match='must not contain `NaN`'):
+            func(
+                training_ts=vertical_no_demand,
+                forecast_interval=forecast_interval,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+            )
+
+    def test_structure_of_returned_dataframe(
+        self, func, vertical_no_demand, forecast_interval,
+    ):
+        """Both `.predict()` return a `pd.DataFrame` with five columns."""
+        result = func(
+            training_ts=vertical_no_demand,
+            forecast_interval=forecast_interval,
+            frequency=test_config.VERTICAL_FREQUENCY_LONG,
+        )
+
+        assert isinstance(result, pd.DataFrame)
+        assert list(result.columns) == [
+            'prediction',
+            'low80',
+            'high80',
+            'low95',
+            'high95',
+        ]
+
+    def test_predict_horizontal_time_series_with_no_demand(
+        self, func, horizontal_no_demand, forecast_time_step,
+    ):
+        """Predicting a horizontal time series with no demand ...
+
+        ... returns a `pd.DataFrame` with five columns holding only `0.0` values.
+        """
+        predictions = func(
+            training_ts=horizontal_no_demand,
+            forecast_interval=forecast_time_step,
+            frequency=7,
+        )
+
+        result = predictions.sum().sum()
+
+        assert result == 0
+
+    def test_predict_vertical_time_series_with_no_demand(
+        self, func, vertical_no_demand, forecast_interval,
+    ):
+        """Predicting a vertical time series with no demand ...
+
+        ... returns a `pd.DataFrame` with five columns holding only `0.0` values.
+        """
+        predictions = func(
+            training_ts=vertical_no_demand,
+            forecast_interval=forecast_interval,
+            frequency=test_config.VERTICAL_FREQUENCY_LONG,
+        )
+
+        result = predictions.sum().sum()
+
+        assert result == 0