- the first notebook runs the tactical-forecasts command
- the second notebook describes the tactical demand forecasting process
+ demand aggregation on a per-pixel level
+ time series generation: horizontal, vertical, and real-time time series
+ STL decomposition into seasonal, trend, and residual components
+ choosing the most promising forecasting model
+ predicting demand with various models
- fix where to re-start the forecasting process after it was interrupted
- enable the heuristic for choosing the most promising model
to also work for 7 training weeks
- ensure a `Restaurant` only has one unique `Order.pickup_address`
- rework `Grid.gridify()` so that only pickup addresses are assigned
into `Pixel`s
- include database migrations to ensure the data adhere to these
tighter constraints
- reorganize `urban_meal_delivery.console` into a sub-package
- move `tests.db.conftest` fixtures into `tests.conftest`
=> some integration tests regarding CLI scripts need a database
- add `urban_meal_delivery.console.decorators.db_revision` decorator
to ensure the database is at a certain state before a CLI script runs
- refactor the `urban_meal_delivery.db.grids.Grid.gridify()` constructor:
- bug fix: even empty `Pixel`s end up in the database temporarily
=> create `Pixel` objects only if an `Address` is to be assigned
to it
- streamline code and docstring
- add further test cases
