- show an example order's path from the restaurant to the customer's
delivery address as it is travelled by a courier
- explain how to use the Google Maps API directly
- show how the API is integrated into the data model
- 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 two notebook files are helpful in visualizing all relevant
pickup (red) or delivery (blue) locations from the point of view
of either e restaurant or a customer
- add notebook that runs the plotting code
- add three visualizations per city:
+ all addresses, colored by zip code
+ all restaurants, incl. the number of received orders
+ all restaurants on a grid with pixel side length of 1000m
- add a Jupyter notebook that allows to install all project-external
dependencies regarding R and R packages
- adjust the GitHub Action workflow to also install R and the R packages
used within the project
- add a `init_r` module that initializes all R packages globally
once the `urban_meal_delivery` package is imported
- remove the factory functions for creating engines and sessions
- define global engine, connection, and session objects to be used
everywhere in the urban_meal_delivery package
