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

src/urban_meal_delivery/forecasts/__init__.py

@@ -1,4 +1,29 @@
-"""Demand forecasting utilities."""
+"""Demand forecasting utilities.
+
+This sub-package is divided into further sub-packages and modules as follows:
+
+`methods` contains various time series related statistical methods, implemented
+as plain `function` objects that are used to predict into the future given a
+time series of historic order counts. The methods are context-agnostic, meaning
+that they only take and return `pd.Series/DataFrame`s holding numbers and
+are not concerned with how these numbers were generated or what they mean.
+Some functions, like `arima.predict()` or `ets.predict()` wrap functions called
+in R using the `rpy2` library. Others, like `extrapolate_season.predict()`, are
+written in plain Python.
+
+`timify` defines an `OrderHistory` class that abstracts away the communication
+with the database and provides `pd.Series` objects with the order counts that
+are fed into the `methods`. In particular, it uses SQL statements behind the
+scenes to calculate the historic order counts on a per-`Pixel` level. Once the
+data is loaded from the database, an `OrderHistory` instance provides various
+ways to slice out, or generate, different kinds of order time series (e.g.,
+"horizontal" vs. "vertical" time series).
+
+`models` defines various forecasting `*Model`s that combine a given kind of
+time series with one of the forecasting `methods`. For example, the ETS method
+applied to a horizontal time series is implemented in the `HorizontalETSModel`.
+"""
 
 from urban_meal_delivery.forecasts import methods
+from urban_meal_delivery.forecasts import models
 from urban_meal_delivery.forecasts import timify