Alexander Hess
9d6de9d98c
- 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 |
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| .. | ||
| papers | ||
| visualizations | ||
| 00_r_dependencies.ipynb | ||
| 01_clean_data.ipynb | ||
| 02_gridification.ipynb | ||
| 03_grid_visualizations.ipynb | ||
| 04_visualizing_restaurants.ipynb | ||
| 05_visualizing_customers.ipynb | ||
| 06_tactical_demand_forecasting.ipynb | ||
| 07_visualizing_demand_forecasting.ipynb | ||