Create tactical demand forecasts

- 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

src/urban_meal_delivery/configuration.py

@@ -49,9 +49,9 @@ class Config:
     TIME_STEPS = [60]
 
     # Training horizons (in full weeks) used to train the forecasting models.
-    # For now, we only use 8 weeks as that was the best performing in
+    # For now, we only use 7 and 8 weeks as that was the best performing in
     # a previous study (note:4f79e8fa).
-    TRAIN_HORIZONS = [8]
+    TRAIN_HORIZONS = [7, 8]
 
     # The demand forecasting methods used in the simulations.
     FORECASTING_METHODS = ['hets', 'rtarima']