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Implemented Non Autorgressive Quantile Regression

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Victor Mylle
2023-11-18 17:42:06 +00:00
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Result-Reports/November_1.md
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@@ -3,17 +3,26 @@
- [x] Compare autoregressive vs non-autoregressive
- [x] Rewrite dataloader for more input parameters (load forecast)
- [ ] Explore more input parameters (load forecast)
- [x] Explore more input parameters (load forecast)
- [x] Quantile Regression sampling fix
- [x] Quantile Regression exploration
- [x] Plots with good scaling (y-axis)
- [x] Some days in load forecast are missing, remove samples from dataset (Implemented a skip in the NRVDataset)
- [ ] Quantile Regression nakijken
- [ ] Test scores voor 96 values
- [x] Quantile Regression nakijken
- [x] Test scores voor 96 values
- [ ] (Optional) Andere modellen (LSTM?)
- [x] Non autoregressive Quantile Regression
- [x] Fix debug plots for quantile regression -> predict quantiles and look if true value is below a quantile, if so 1 else 0 and average these over all samples
- [ ] Full day debug plots for quantile regression
- [ ] CPRS Metrics
- [ ] Time as input parameter:
- [ ] Cosine per year, day,
- [ ] 4 Quarter features
- [ ] Probabilistic Baseline -> Quantiles on Training Data -> Breedte bekijken -> Gebruiken voor CPRS en plotjes
- Day-ahead implicit net position( ())
## 2. Autoregressive vs Non-Autoregressive
Training data: 2015 - 2022 \
@@ -99,3 +108,41 @@ Also tried one with dropout of 0.3, results are better. This needs to be tested
| Quantiles | Train-MAE | Train-MSE | Test-MAE | Test-MSE |
| --- | --- | --- | --- | --- |
| 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 | 63.710736942371994 | 6988.6436956508105 | 77.29499496466444 | 10706.484005597813 |
# More input parameters
## Non-Autoregressive
Learning Rate: 0.0003 \
Batch Size: 1024 \
Early Stopping: 15 \
Trining Data: 2015 - 2022 \
Hidden Layers: 3 \
Hidden Units: 1024
| Input Parameters | Experiment | Train-MAE | Train-MSE | Test-MAE | Test-MSE |
| --- | --- | --- | --- | --- | --- |
| NRV History | [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/cede0babadbb41d49dbcfd6ade78cd5e/info-output/metrics/scalar?columns=selected&columns=type&columns=name&columns=tags&columns=status&columns=project.name&columns=users&columns=started&columns=last_update&columns=last_iteration&columns=parent.name&order=-last_update&filter=) | 94.40104675292969 | 15815.435546875 | 104.87157440185547 | 21358.115234375 |
| NRV History + Load History | [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/aa7768bf0c054f55a55d99c15b3f0f25/info-output/metrics/scalar?columns=selected&columns=type&columns=name&columns=tags&columns=status&columns=project.name&columns=users&columns=started&columns=last_update&columns=last_iteration&columns=parent.name&order=-last_update&filter=) | 92.76568603515625 | 15250.294921875 | 104.78298950195312 | 21043.908203125 |
| NRV History + Load History + Load Forecast | [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/b213c7863f0f479f80bf612a9259c533/info-output/metrics/scalar?columns=selected&columns=type&columns=name&columns=tags&columns=status&columns=project.name&columns=users&columns=started&columns=last_update&columns=last_iteration&columns=parent.name&order=-last_update&filter=) | 92.92375946044922 | 15294.857421875 | 104.52192687988281 | 20994.251953125 |
# Quantile Regression Debugging
[Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/50d0c7922faa4681816635c9cfa3eb72/info-output/debugImages?columns=selected&columns=type&columns=name&columns=tags&columns=status&columns=project.name&columns=users&columns=started&columns=last_update&columns=last_iteration&columns=parent.name&order=-last_update&filter=)
# Scores on full day prediction
Only history NRV as features
Learning Rate: 0.0003 \
Batch Size: 1024 \
Early Stopping: 15 \
Trining Data: 2015 - 2022 \
Hidden Layers: 3 \
Hidden Units: 1024
| Model | Experiment | Train-MAE | Train-MSE | Test-MAE | Test-MSE |
| --- | --- | --- | --- | --- | --- |
| Non-Autoregressive | [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/097b3832eb5e4c5a8fa2e04887975c29/execution?columns=selected&columns=type&columns=name&columns=tags&columns=status&columns=project.name&columns=users&columns=started&columns=last_update&columns=last_iteration&columns=parent.name&order=-last_update&filter=) | 94.52633666992188 | 15835.671875 | 104.07229614257812 | 21090.9765625 |
| Auto-Regressive | [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/65748b5d30054d3381a76e2c5a73e107/info-output/metrics/scalar?columns=selected&columns=type&columns=name&columns=tags&columns=status&columns=project.name&columns=users&columns=started&columns=last_update&columns=last_iteration&columns=parent.name&order=-last_update&filter=) | - | - | 105.1736068725586 | 21376.697265625 |
| Quantile Regression | [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/df5669968cf64c42ba7a97fc2d745b76/info-output/metrics/scalar?columns=selected&columns=type&columns=name&columns=tags&columns=status&columns=project.name&columns=users&columns=started&columns=last_update&columns=last_iteration&columns=parent.name&order=-last_update&filter=) | - | - | 105.53107468002209 | 21656.24950570062 |