Add OrderHistory.make_horizontal_time_series()

- the method slices out a horizontal time series from the data within
  an `OrderHistory` object

src/urban_meal_delivery/forecasts/timify.py

@@ -1,6 +1,7 @@
 """Obtain and work with time series data."""
 
 import datetime as dt
+from typing import Tuple
 
 import pandas as pd
 
@@ -145,3 +146,69 @@ class OrderHistory:
         index.names = ['pixel_id', 'start_at']
 
         return data.reindex(index, fill_value=0)
+
+    def make_horizontal_time_series(  # noqa:WPS210
+        self, pixel_id: int, predict_at: dt.datetime, train_horizon: int,
+    ) -> Tuple[pd.DataFrame, int, int]:
+        """Slice a horizontal time series out of the `.totals`.
+
+        Create a time series covering `train_horizon` weeks that can be used
+        for training a forecasting model to predict the demand at `predict_at`.
+
+        For explanation of the terms "horizontal", "vertical", and "real-time"
+        in the context of time series, see section 3.2 in the following paper:
+        https://github.com/webartifex/urban-meal-delivery-demand-forecasting/blob/main/paper.pdf
+
+        Args:
+            pixel_id: pixel in which the time series is aggregated
+            predict_at: time step (i.e., "start_at") for which a prediction is made
+            train_horizon: weeks of historic data used to predict `predict_at`
+
+        Returns:
+            training time series, frequency, actual order count at `predict_at`
+
+        Raises:
+            LookupError: `pixel_id` is not in the `grid`
+            RuntimeError: desired time series slice is not entirely in `.totals`
+        """
+        try:
+            intra_pixel = self.totals.loc[pixel_id]
+        except KeyError:
+            raise LookupError('The `pixel_id` is not in the `grid`') from None
+
+        if predict_at >= config.CUTOFF_DAY:  # pragma: no cover
+            raise RuntimeError('Internal error: cannot predict beyond the given data')
+
+        # The first and last training day are just before the `predict_at` day
+        # and span exactly `train_horizon` weeks covering only the times of the
+        # day equal to the hour/minute of `predict_at`.
+        first_train_day = predict_at.date() - dt.timedelta(weeks=train_horizon)
+        first_start_at = dt.datetime(
+            first_train_day.year,
+            first_train_day.month,
+            first_train_day.day,
+            predict_at.hour,
+            predict_at.minute,
+        )
+        last_train_day = predict_at.date() - dt.timedelta(days=1)
+        last_start_at = dt.datetime(
+            last_train_day.year,
+            last_train_day.month,
+            last_train_day.day,
+            predict_at.hour,
+            predict_at.minute,
+        )
+
+        # The frequency is the number of weekdays.
+        frequency = 7
+
+        # Take only the counts at the `predict_at` time.
+        training_df = intra_pixel.loc[
+            first_start_at : last_start_at : self._n_daily_time_steps  # type: ignore
+        ]
+        if len(training_df) != frequency * train_horizon:
+            raise RuntimeError('Not enough historic data for `predict_at`')
+
+        actual_df = intra_pixel.loc[[predict_at]]
+
+        return training_df, frequency, actual_df