Add wrappers for R's "arima" and "ets" functions

tests/forecasts/conftest.py

@@ -0,0 +1,76 @@
+"""Fixtures and globals for testing `urban_meal_delivery.forecasts`."""
+
+import datetime as dt
+
+import pandas as pd
+import pytest
+
+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.
+
+    The times resemble a horizontal time series with a `frequency` of `7`.
+    All observations take place at `NOON`.
+    """
+    first_start_at = dt.datetime(
+        test_config.YEAR, test_config.MONTH, test_config.DAY, test_config.NOON, 0,
+    )
+
+    gen = (
+        start_at
+        for start_at in pd.date_range(first_start_at, test_config.END, freq='D')
+    )
+
+    index = pd.Index(gen)
+    index.name = 'start_at'
+
+    assert len(index) == 15  # sanity check
+
+    return index
+
+
+@pytest.fixture
+def horizontal_no_demand(horizontal_datetime_index):
+    """A horizontal time series of order totals when there was no demand."""
+    return pd.Series(0, index=horizontal_datetime_index, name='order_totals')
+
+
+@pytest.fixture
+def vertical_datetime_index():
+    """A `pd.Index` with `DateTime` values.
+
+    The times resemble a vertical time series with a
+    `frequency` of `7` times the number of daily time steps,
+    which is `12` for `LONG_TIME_STEP` values.
+    """
+    gen = (
+        start_at
+        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
+    )
+
+    index = pd.Index(gen)
+    index.name = 'start_at'
+
+    assert len(index) == 15 * 12  # sanity check
+
+    return index
+
+
+@pytest.fixture
+def vertical_no_demand(vertical_datetime_index):
+    """A vertical time series of order totals when there was no demand."""
+    return pd.Series(0, index=vertical_datetime_index, name='order_totals')