NeuralForecast
and
HINT
class, to create fit, predict and reconcile forecasts.
We will use the TourismL dataset that summarizes large Australian
national visitor survey.
OutlineGeographical Division | Number of series per division | Number of series per purpose | Total |
---|---|---|---|
Australia | 1 | 4 | 5 |
States | 7 | 28 | 35 |
Zones | 27 | 108 | 135 |
Regions | 76 | 304 | 380 |
Total | 111 | 444 | 555 |
BottomUp
and MinTrace
reconciliation techniques:
level | metric | NHITS | AutoARIMA | |
---|---|---|---|---|
0 | Country | scaled_crps | 0.044431 | 0.131136 |
1 | Country/State | scaled_crps | 0.063411 | 0.147516 |
2 | Country/State/Zone | scaled_crps | 0.106060 | 0.174071 |
3 | Country/State/Zone/Region | scaled_crps | 0.151988 | 0.205654 |
4 | Country/Purpose | scaled_crps | 0.075821 | 0.133664 |
5 | Country/State/Purpose | scaled_crps | 0.114674 | 0.181850 |
6 | Country/State/Zone/Purpose | scaled_crps | 0.180491 | 0.244324 |
7 | Country/State/Zone/Region/Purpose | scaled_crps | 0.245466 | 0.310656 |
8 | Overall | scaled_crps | 0.122793 | 0.191109 |