alexander/urban-meal-delivery
1
1
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity Actions

Create tactical demand forecasts

Browse source 
- 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
This commit is contained in:
Alexander Hess 2021-02-04 23:41:21 +01:00
commit 9d6de9d98c
Signed by: alexander
GPG key ID: 344EA5AB10D868E0
5 changed files with 3480 additions and 4 deletions
Download patch file Download diff file Expand all files Collapse all files

4
src/urban_meal_delivery/forecasts/timify.py
View file

@ -542,9 +542,9 @@ class OrderHistory:
pixel_id=pixel_id, predict_day=predict_day, train_horizon=train_horizon,
)
# For now, we only make forecasts with 8 weeks
# For now, we only make forecasts with 7 and 8 weeks
# as the training horizon (note:4f79e8fa).
if train_horizon == 8:
if train_horizon == 7 or train_horizon == 8:
if add >= 25: # = "high demand"
return models.HorizontalETSModel(order_history=self)
elif add >= 10: # = "medium demand"