- 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
- the method implements a heuristic from the first research paper
that chooses the most promising forecasting `*Model` based on
the average daily demand in a `Pixel` for a given `train_horizon`
- adjust the test scenario => `LONG_TRAIN_HORIZON` becomes `8`
as that is part of the rule implemented in the heuristic
- the main purpose of this class is to manage querying the order totals
from the database and slice various kinds of time series out of the
data
- the class holds the former `aggregate_orders()` function as a method
- modularize the corresponding tests
- add `tests.config` with globals used when testing to provide a
single source of truth for various settings
- the function queries the database and aggregates the ad-hoc orders
by pixel and time steps into a demand time series
- implement "heavy" integration tests for `aggregate_orders()`
- make `pandas` a package dependency
- streamline the `Config`
