Add urban_meal_delivery.forecasts.models sub-package

- `*Model`s use the `methods.*.predict()` functions to predict demand
  given an order time series generated by `timify.OrderHistory`
- `models.base.ForecastingModelABC` unifies how all `*Model`s work
  and implements a caching strategy
- implement three `*Model`s for tactical forecasting, based on the
  hets, varima, and rtarima models described in the first research paper
- add overall documentation for `urban_meal_delivery.forecasts` package
- move the fixtures in `tests.forecasts.timify.conftest` to
  `tests.forecasts.conftest` and adjust the horizon of the test horizon
  from two to three weeks

tests/forecasts/conftest.py

@@ -1,4 +1,4 @@
-"""Fixtures and globals for testing `urban_meal_delivery.forecasts`."""
+"""Fixtures for testing the `urban_meal_delivery.forecasts` sub-package."""
 
 import datetime as dt
 
@@ -7,6 +7,7 @@ import pytest
 
 from tests import config as test_config
 from urban_meal_delivery import config
+from urban_meal_delivery.forecasts import timify
 
 
 @pytest.fixture
@@ -28,7 +29,10 @@ def horizontal_datetime_index():
     index = pd.Index(gen)
     index.name = 'start_at'
 
-    assert len(index) == 15  # sanity check
+    # Sanity check.
+    # `+1` as both the `START` and `END` day are included.
+    n_days = (test_config.END - test_config.START).days + 1
+    assert len(index) == n_days
 
     return index
 
@@ -58,7 +62,10 @@ def vertical_datetime_index():
     index = pd.Index(gen)
     index.name = 'start_at'
 
-    assert len(index) == 15 * 12  # sanity check
+    # Sanity check: n_days * n_number_of_opening_hours.
+    # `+1` as both the `START` and `END` day are included.
+    n_days = (test_config.END - test_config.START).days + 1
+    assert len(index) == n_days * 12
 
     return index
 
@@ -67,3 +74,54 @@ def vertical_datetime_index():
 def vertical_no_demand(vertical_datetime_index):
     """A vertical time series with order totals: no demand."""
     return pd.Series(0, index=vertical_datetime_index, name='n_orders')
+
+
+@pytest.fixture
+def good_pixel_id(pixel):
+    """A `pixel_id` that is on the `grid`."""
+    return pixel.id  # `== 1`
+
+
+@pytest.fixture
+def order_totals(good_pixel_id):
+    """A mock for `OrderHistory.totals`.
+
+    To be a bit more realistic, we sample two pixels on the `grid`.
+
+    Uses the LONG_TIME_STEP as the length of a time step.
+    """
+    pixel_ids = [good_pixel_id, good_pixel_id + 1]
+
+    gen = (
+        (pixel_id, start_at)
+        for pixel_id in pixel_ids
+        for start_at in pd.date_range(
+            test_config.START, test_config.END, freq=f'{test_config.LONG_TIME_STEP}T',
+        )
+        if config.SERVICE_START <= start_at.hour < config.SERVICE_END
+    )
+
+    # Re-index `data` filling in `0`s where there is no demand.
+    index = pd.MultiIndex.from_tuples(gen)
+    index.names = ['pixel_id', 'start_at']
+
+    df = pd.DataFrame(data={'n_orders': 1}, index=index)
+
+    # Sanity check: n_pixels * n_time_steps_per_day * n_days.
+    # `+1` as both the `START` and `END` day are included.
+    n_days = (test_config.END - test_config.START).days + 1
+    assert len(df) == 2 * 12 * n_days
+
+    return df
+
+
+@pytest.fixture
+def order_history(order_totals, grid):
+    """An `OrderHistory` object that does not need the database.
+
+    Uses the LONG_TIME_STEP as the length of a time step.
+    """
+    oh = timify.OrderHistory(grid=grid, time_step=test_config.LONG_TIME_STEP)
+    oh._data = order_totals
+
+    return oh