87 lines
5.1 KiB
Markdown
87 lines
5.1 KiB
Markdown
# Result Report November
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## 1. TODOs
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- [x] Compare autoregressive vs non-autoregressive
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- [ ] Add more input parameters (load forecast)
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- [x] Quantile Regression sampling fix
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- [x] Quantile Regression exploration
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## 2. Autoregressive vs Non-Autoregressive
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Training data: 2015 - 2022 \
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Batch_size: 1024 \
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Learning_rate: 0.0003 \
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Early_stopping: 10
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### 2.1 Linear Model
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Comparison: [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/compare-experiments;ids=3b512226b24c46199b584d7abc23bf96,097b3832eb5e4c5a8fa2e04887975c29/scalars/values?scalars=values)
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<!-- table with 3 columns and rows: experiment, Train-MAE, Train-MSE, Test-MAE, Test-MSE -->
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| | Autoregressive | Non-Autoregressive |
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| --- | --- | --- |
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| Experiment (ClearML) | [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/3b512226b24c46199b584d7abc23bf96/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=) | [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=) |
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| Train-MAE | 68.0202865600586 | 94.83179473876953 |
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| Train-MSE | 7861.2197265625 | 15977.8759765625 |
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| Test-MAE | 78.05316925048828 | 104.11575317382812 |
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| Test-MSE | 10882.755859375 | 21145.583984375 |
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### 2.2 Non Linear Model
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Hidden layers: 1 \
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Hidden units: 512
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Comparison: [Link](hhttps://clearml.victormylle.be/projects/135c055e64e54486a54055f33ca9a5af/compare-experiments;ids=57f4a09c6ce54296b6e034f2e0236420,b5f6862f900948c18a834a1a970c8710/scalars/values?scalars=values)
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| | Autoregressive | Non-Autoregressive |
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| --- | --- | --- |
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| Experiment (ClearML) | [Link](https://clearml.victormylle.be/projects/135c055e64e54486a54055f33ca9a5af/experiments/57f4a09c6ce54296b6e034f2e0236420/output/execution) | [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/b5f6862f900948c18a834a1a970c8710/output/execution) |
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| Train-MAE | 66.78179931640625 | 94.52633666992188 |
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| Train-MSE | 7507.53955078125 | 15835.671875 |
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| Test-MAE | 77.63729095458984 | 104.07229614257812 |
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| Test-MSE | 10789.544921875 | 21090.9765625 |
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Also tried with [3 hidden layers for Non-Autoregressive model](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/583d0bc1dfb7494d81cf356f6d003dbb/output/execution), test results didn't improve.
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# 3. Quantile Regression
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## 3.1 Sampling Fix
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The model outputs values for the quantiles it is trained on. For example, the quantiles [0.025, 0.05, 0.1, 0.15, 0.85, 0.9, 0.95, 0.975] can be used. The values outputted by the model are like: [-0.23013, -0.19831, -0.15217, -0.13654, 0.011687, 0.015129, 0.043187, 0.047704]. Plotting these as CDF:
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\
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*Plotted output values with cubic interpolation*
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Samling from a uniform distribution, we can convert it to our distribution by using the inverse CDF. In python we can interpolate immediately by switching the x and y axis. This gives us the following code:
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```
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interp1d(quantiles, output_values, kind='quadratic', bounds_error=False, fill_value="extrapolate")
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```
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The mean of x amount of samples can be calculated.
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## 3.2 Exploration
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### 3.2.1 Linear Model
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Learning Rate: 0.0003 \
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Batch Size: 1024 \
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Early Stopping: 10 \
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Trining Data: 2015 - 2022
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| Quantiles | Train-MAE | Train-MSE | Test-MAE | Test-MSE |
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| --- | --- | --- | --- | --- |
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| 0.025, 0.1, 0.2, 0.3, 0.5, 0.6, 0.8, 0.85, 0.9, 0.975 | 68.07254628777868 | 7872.668472187121 | 78.11135584669907 | 10903.793883789216 |
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| 0.025, 0.1, 0.15, 0.2, 0.5, 0.8, 0.85, 0.9, 0.975 | 68.0732244289865 | 7873.212834241974 | 78.1143230666738 | 10907.350919114313 |
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| 0.025, 0.05, 0.1, 0.15, 0.85, 0.9, 0.95, 0.975 | 68.2798014428824 | 7936.7644114273935 | 78.50109206637464 | 11005.706457116454 |
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| 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 | 68.06139224378171 | 7871.467571921973 | 78.10843456751378 | 10904.55519059502 |
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| 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9 | 68.0350635204691 | 7868.661882749334 | 78.1139478264609 | 10905.562798801011 |
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| 0.1, 0.2, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.8, 0.9 | 68.03191172170285 | 7868.483061240721 | 78.13204232055722 | 10908.837301340453 |
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These are all very close
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### 3.2.2 Non Linear Model
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Learning Rate: 0.0003 \
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Batch Size: 1024 \
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Early Stopping: 10 \
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Trining Data: 2015 - 2022 \
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Hidden Layers: 3 \
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Hidden Units: 1024
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| Quantiles | Train-MAE | Train-MSE | Test-MAE | Test-MSE |
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| --- | --- | --- | --- | --- |
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| 0.1, 0.2, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.8, 0.9 | 65.6542529596313 | 7392.5142575554955 | 77.55779692831604 | 10769.161724849037 |
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| 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 | 65.68495924348356 | 7326.2239225611975 | 77.62433888969542 | 10789.003223366473 | |