Comments (11)
jdb78
commented on August 30, 2024
1
The first error is due to an unfortunate default in the from_dataset method. The default was predict=True which causes to the dataset to select the last samples per timeseries for prediction - as you only have one timeseries there is only one sample). I changed the default to predict=False. You might want to pass now stop_randomization=True for the validation dataset.
The second error is a genuine error (1 off error) which I just fixed. Thanks for bringing this to my attention! The fix is pushed to master, so installing from git should do the job.
from pytorch-forecasting.
RaviKumarAndroid
commented on August 30, 2024
Please help me how to use the attached dataset through the forecasting library.
and even though if the batch is okay(with predict_mode=False) another error occurs :
Traceback (most recent call last):
File "/Users/wilson/PycharmProjects/forecast_test/predictors/temporal_fusion_transformer/main_torch_forecasting.py", line 159, in <module>
tft, train_dataloader=train_dataloader, val_dataloaders=val_dataloader
File "/usr/local/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1044, in fit
results = self.run_pretrain_routine(model)
File "/usr/local/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1196, in run_pretrain_routine
False)
File "/usr/local/lib/python3.7/site-packages/pytorch_lightning/trainer/evaluation_loop.py", line 272, in _evaluate
for batch_idx, batch in enumerate(dataloader):
File "/usr/local/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
data = self._next_data()
File "/usr/local/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 385, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/usr/local/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/Users/wilson/PycharmProjects/forecast_test/pytorch_forecasting/data.py", line 385, in __getitem__
assert decoder_length >= self.min_prediction_length
AssertionError
from pytorch-forecasting.
RaviKumarAndroid
commented on August 30, 2024
Thanks Man! Really appreciate your work.
Got another error after predict=False
Another 1 off error
File "/Users/wilson/PycharmProjects/forecast_test/main_torch_forecasting.py", line 154, in <module>
tft, train_dataloader=train_dataloader, val_dataloaders=val_dataloader
File "/usr/local/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1044, in fit
results = self.run_pretrain_routine(model)
File "/usr/local/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1196, in run_pretrain_routine
False)
File "/usr/local/lib/python3.7/site-packages/pytorch_lightning/trainer/evaluation_loop.py", line 293, in _evaluate
output = self.evaluation_forward(model, batch, batch_idx, dataloader_idx, test_mode)
File "/usr/local/lib/python3.7/site-packages/pytorch_lightning/trainer/evaluation_loop.py", line 470, in evaluation_forward
output = model.validation_step(*args)
File "/Users/wilson/PycharmProjects/forecast_test/pytorch_forecasting/models/base_model.py", line 126, in validation_step
log, _ = self.step(x, y, batch_idx, label="val")
File "/Users/wilson/PycharmProjects/forecast_test/pytorch_forecasting/models/temporal_fusion_transformer/__init__.py", line 544, in step
attention_prediction_horizon=0, # attention only for first prediction horizon
File "/Users/wilson/PycharmProjects/forecast_test/pytorch_forecasting/models/temporal_fusion_transformer/__init__.py", line 576, in interpret_output
encoder_length_histogram = integer_histogram(out["encoder_lengths"], min=0, max=self.hparams.max_encoder_length)
File "/Users/wilson/PycharmProjects/forecast_test/pytorch_forecasting/utils.py", line 27, in integer_histogram
dim=0, index=uniques - min, src=counts
RuntimeError: index 37 is out of bounds for dimension 0 with size 37
Thanks Again
from pytorch-forecasting.
jdb78
commented on August 30, 2024
This is probably due to only having a single timeseries in the dataset. I will have a closer look later or on the weekend. You could probably run with log_interval=-1 in the meantime and skip the interpretation step alltogether.
from pytorch-forecasting.
RaviKumarAndroid
commented on August 30, 2024
Yeah, For Now I commented out just the interpretation part because i would like to be able to see prediction outputs in tensorboard. But I found an issue in the visualisation.
I Think. You are plotting Non Normalised target vs output(not yet denormalised).
from pytorch-forecasting.
jdb78
commented on August 30, 2024
Sorry for taking a bit of time. The plotting functionality is not broken. However, I think there are two issues here:
- The network has probably not trained for long enough
- To train, timeseries should be normalized (I think almost all networks require this) for faster convergence and better generalisation.
I am working on this normalization so you do not have to take care of it manually.
from pytorch-forecasting.
RaviKumarAndroid
commented on August 30, 2024
Thanks, I am not normalising manually. I read the code and i saw you are already normalising them. But while plotting its plotting like above.
Actually when i printed the x["encoder_target"][0], x["decoder_target"][0] I found those values to be not normalised
for batch, _ in test_dataloader:
output = tft(batch)
print(batch['encoder_target'][0])
print(batch['decoder_target'][0])
print(output['prediction'][0])
break
This is what gets printed
tensor([309.5000, 309.5000, 309.4500, 309.5000, 309.4500, 309.4500, 309.4000,
309.4000, 309.3500, 309.3000, 309.2500, 309.3000, 309.4000, 309.4000,
309.4000, 309.3000, 309.1000, 309.2500, 309.3500, 309.4500, 309.5500,
309.5500, 309.5000, 309.4500, 309.2500, 309.2500, 309.5500, 309.5000,
309.4500, 309.6500, 309.6000, 309.6000, 309.5000, 309.5500, 309.5500,
309.6500, 309.7500, 309.8000, 309.7000, 309.5000, 309.7000])
tensor([310.9000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])
tensor([[-0.4033, -0.5201, 1.2264, -0.3546, -0.1055, 0.1809, -0.7248],
[-0.2388, 0.1727, -0.3465, -0.3504, 0.4635, 0.3774, -0.0414],
[-0.2694, 0.1961, -0.3326, -0.2950, 0.4649, 0.2898, -0.0930],
[-0.2943, 0.1073, -0.2853, -0.4206, 0.6087, 0.3669, -0.2790],
[-0.2427, -0.0640, -0.3871, -0.2410, 0.5516, 0.6015, -0.3814],
[-0.3132, 0.1434, -0.1311, -0.4787, 0.5760, 0.4773, -0.2528]],
grad_fn=<SelectBackward>)
The scale of the values is wrong here
I believe its happening due to "target" being saved non scaled in the dataframe??
See the batch :
{'encoder_cat': tensor([[[0, 5, 3],
[0, 5, 3],
[0, 5, 3],
...,
[0, 6, 3],
[0, 6, 3],
[0, 6, 3]],
[[0, 5, 3],
[0, 5, 3],
[0, 5, 3],
...,
[0, 6, 3],
[0, 6, 3],
[0, 0, 0]],
[[0, 5, 3],
[0, 5, 3],
[0, 5, 3],
...,
[0, 6, 3],
[0, 0, 0],
[0, 0, 0]],
...,
[[0, 1, 4],
[0, 1, 4],
[0, 1, 4],
...,
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
[[0, 1, 4],
[0, 1, 4],
[0, 1, 4],
...,
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
[[0, 1, 4],
[0, 1, 4],
[0, 1, 4],
...,
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]]]), 'encoder_cont': tensor([[[ 0.9753, -1.1389, -1.3834, ..., -1.3258, -1.3772, -0.2323],
[ 0.9759, -1.1111, -1.3834, ..., -1.3258, -1.3772, -0.3194],
[ 0.9765, -1.0833, -1.3834, ..., -1.3721, -1.4003, 0.1851],
...,
[ 0.9988, -1.0000, -1.2444, ..., -1.2334, -1.2846, 0.8774],
[ 0.9994, -1.0000, -1.3139, ..., -1.3721, -1.3772, 3.0574],
[ 1.0000, -1.0000, -1.3834, ..., -1.3258, -1.2846, 1.0244]],
[[ 0.9759, -1.1111, -1.3834, ..., -1.3258, -1.3772, -0.3194],
[ 0.9765, -1.0833, -1.3834, ..., -1.3721, -1.4003, 0.1851],
[ 0.9772, -1.0556, -1.4066, ..., -1.3952, -1.3772, 0.1334],
...,
[ 0.9994, -1.0000, -1.3139, ..., -1.3721, -1.3772, 3.0574],
[ 1.0000, -1.0000, -1.3834, ..., -1.3258, -1.2846, 1.0244],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
[[ 0.9765, -1.0833, -1.3834, ..., -1.3721, -1.4003, 0.1851],
[ 0.9772, -1.0556, -1.4066, ..., -1.3952, -1.3772, 0.1334],
[ 0.9778, -1.0278, -1.3834, ..., -1.4183, -1.4003, 0.7037],
...,
[ 1.0000, -1.0000, -1.3834, ..., -1.3258, -1.2846, 1.0244],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
...,
[[ 1.0525, -1.0000, 0.1688, ..., 0.1534, 0.1499, -0.4531],
[ 1.0531, -1.0000, 0.1457, ..., 0.1765, 0.1499, -0.4374],
[ 1.0537, -1.0000, 0.1457, ..., 0.1303, 0.0805, -0.5453],
...,
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
[[ 1.0531, -1.0000, 0.1457, ..., 0.1765, 0.1499, -0.4374],
[ 1.0537, -1.0000, 0.1457, ..., 0.1303, 0.0805, -0.5453],
[ 1.0543, -0.9722, 0.0993, ..., 0.1303, 0.1499, -0.4982],
...,
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
[[ 1.0537, -1.0000, 0.1457, ..., 0.1303, 0.0805, -0.5453],
[ 1.0543, -0.9722, 0.0993, ..., 0.1303, 0.1499, -0.4982],
[ 1.0550, -0.9444, 0.1225, ..., 0.1534, 0.1036, -0.5765],
...,
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]]]), 'encoder_target': tensor([[309.5000, 309.5000, 309.4500, ..., 309.7000, 309.5000, 309.7000],
[309.5000, 309.4500, 309.5000, ..., 309.5000, 309.7000, 0.0000],
[309.4500, 309.5000, 309.4500, ..., 309.7000, 0.0000, 0.0000],
...,
[312.8000, 312.8000, 312.6500, ..., 0.0000, 0.0000, 0.0000],
[312.8000, 312.6500, 312.8000, ..., 0.0000, 0.0000, 0.0000],
[312.6500, 312.8000, 312.7000, ..., 0.0000, 0.0000, 0.0000]]), 'encoder_lengths': tensor([41, 40, 39, 38, 37, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36,
36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36,
36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36,
36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36,
36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36,
36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36,
36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36,
36, 36]), 'decoder_cat': tensor([[[0, 0, 4],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
[[0, 0, 4],
[0, 0, 4],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
[[0, 0, 4],
[0, 0, 4],
[0, 0, 4],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
...,
[[0, 2, 4],
[0, 2, 4],
[0, 2, 4],
[0, 2, 4],
[0, 2, 4],
[0, 2, 4]],
[[0, 2, 4],
[0, 2, 4],
[0, 2, 4],
[0, 2, 4],
[0, 2, 4],
[0, 2, 4]],
[[0, 2, 4],
[0, 2, 4],
[0, 2, 4],
[0, 2, 4],
[0, 2, 4],
[0, 2, 4]]]), 'decoder_cont': tensor([[[ 1.0006, -1.0000, -1.2907, ..., -1.2334, -0.7293, 3.8031],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
[[ 1.0006, -1.0000, -1.2907, ..., -1.2334, -0.7293, 3.8031],
[ 1.0012, -1.0000, -0.7347, ..., -0.6787, -0.5905, 0.2554],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
[[ 1.0006, -1.0000, -1.2907, ..., -1.2334, -0.7293, 3.8031],
[ 1.0012, -1.0000, -0.7347, ..., -0.6787, -0.5905, 0.2554],
[ 1.0019, -1.0000, -0.5957, ..., -0.6325, -0.5905, 0.9632],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
...,
[[ 1.0747, -0.0556, 0.4237, ..., 0.4076, 0.4044, -0.5491],
[ 1.0753, -0.0278, 0.4005, ..., 0.4307, 0.4044, -0.1958],
[ 1.0759, 0.0000, 0.3774, ..., 0.3614, 0.3118, -0.5494],
[ 1.0766, 0.0278, 0.2847, ..., 0.3383, 0.3813, -0.4259],
[ 1.0772, 0.0556, 0.3774, ..., 0.4307, 0.3813, -0.6217],
[ 1.0778, 0.0833, 0.4237, ..., 0.4076, 0.3581, -0.7255]],
[[ 1.0753, -0.0278, 0.4005, ..., 0.4307, 0.4044, -0.1958],
[ 1.0759, 0.0000, 0.3774, ..., 0.3614, 0.3118, -0.5494],
[ 1.0766, 0.0278, 0.2847, ..., 0.3383, 0.3813, -0.4259],
[ 1.0772, 0.0556, 0.3774, ..., 0.4307, 0.3813, -0.6217],
[ 1.0778, 0.0833, 0.4237, ..., 0.4076, 0.3581, -0.7255],
[ 1.0784, 0.1111, 0.4005, ..., 0.3614, 0.3581, -0.4660]],
[[ 1.0759, 0.0000, 0.3774, ..., 0.3614, 0.3118, -0.5494],
[ 1.0766, 0.0278, 0.2847, ..., 0.3383, 0.3813, -0.4259],
[ 1.0772, 0.0556, 0.3774, ..., 0.4307, 0.3813, -0.6217],
[ 1.0778, 0.0833, 0.4237, ..., 0.4076, 0.3581, -0.7255],
[ 1.0784, 0.1111, 0.4005, ..., 0.3614, 0.3581, -0.4660],
[ 1.0790, 0.1389, 0.3542, ..., 0.4076, 0.3813, -0.4521]]]), 'decoder_target': tensor([[310.9000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[310.9000, 311.2000, 0.0000, 0.0000, 0.0000, 0.0000],
[310.9000, 311.2000, 311.2000, 0.0000, 0.0000, 0.0000],
[310.9000, 311.2000, 311.2000, 311.4000, 0.0000, 0.0000],
[310.9000, 311.2000, 311.2000, 311.4000, 311.7500, 0.0000],
[310.9000, 311.2000, 311.2000, 311.4000, 311.7500, 311.7500],
[311.2000, 311.2000, 311.4000, 311.7500, 311.7500, 311.9000],
[311.2000, 311.4000, 311.7500, 311.7500, 311.9000, 311.9000],
[311.4000, 311.7500, 311.7500, 311.9000, 311.9000, 312.1000],
[311.7500, 311.7500, 311.9000, 311.9000, 312.1000, 312.3000],
[311.7500, 311.9000, 311.9000, 312.1000, 312.3000, 312.3000],
[311.9000, 311.9000, 312.1000, 312.3000, 312.3000, 312.1500],
[311.9000, 312.1000, 312.3000, 312.3000, 312.1500, 311.8500],
[312.1000, 312.3000, 312.3000, 312.1500, 311.8500, 312.0500],
[312.3000, 312.3000, 312.1500, 311.8500, 312.0500, 311.8500],
[312.3000, 312.1500, 311.8500, 312.0500, 311.8500, 312.0500],
[312.1500, 311.8500, 312.0500, 311.8500, 312.0500, 312.0000],
[311.8500, 312.0500, 311.8500, 312.0500, 312.0000, 312.7500],
[312.0500, 311.8500, 312.0500, 312.0000, 312.7500, 312.7500],
[311.8500, 312.0500, 312.0000, 312.7500, 312.7500, 312.6500],
[312.0500, 312.0000, 312.7500, 312.7500, 312.6500, 312.6000],
[312.0000, 312.7500, 312.7500, 312.6500, 312.6000, 312.5000],
[312.7500, 312.7500, 312.6500, 312.6000, 312.5000, 312.3500],
[312.7500, 312.6500, 312.6000, 312.5000, 312.3500, 312.5000],
[312.6500, 312.6000, 312.5000, 312.3500, 312.5000, 312.4000],
[312.6000, 312.5000, 312.3500, 312.5000, 312.4000, 312.4500],
[312.5000, 312.3500, 312.5000, 312.4000, 312.4500, 312.3500],
[312.3500, 312.5000, 312.4000, 312.4500, 312.3500, 312.4000],
[312.5000, 312.4000, 312.4500, 312.3500, 312.4000, 312.6000],
[312.4000, 312.4500, 312.3500, 312.4000, 312.6000, 312.5500],
[312.4500, 312.3500, 312.4000, 312.6000, 312.5500, 312.7000],
[312.3500, 312.4000, 312.6000, 312.5500, 312.7000, 312.7000],
[312.4000, 312.6000, 312.5500, 312.7000, 312.7000, 312.6500],
[312.6000, 312.5500, 312.7000, 312.7000, 312.6500, 312.6000],
[312.5500, 312.7000, 312.7000, 312.6500, 312.6000, 312.4500],
[312.7000, 312.7000, 312.6500, 312.6000, 312.4500, 312.5500],
[312.7000, 312.6500, 312.6000, 312.4500, 312.5500, 312.6500],
[312.6500, 312.6000, 312.4500, 312.5500, 312.6500, 312.6500],
[312.6000, 312.4500, 312.5500, 312.6500, 312.6500, 312.6000],
[312.4500, 312.5500, 312.6500, 312.6500, 312.6000, 312.5000],
[312.5500, 312.6500, 312.6500, 312.6000, 312.5000, 312.3500],
[312.6500, 312.6500, 312.6000, 312.5000, 312.3500, 312.5000],
[312.6500, 312.6000, 312.5000, 312.3500, 312.5000, 312.8000],
[312.6000, 312.5000, 312.3500, 312.5000, 312.8000, 312.9000],
[312.5000, 312.3500, 312.5000, 312.8000, 312.9000, 313.2500],
[312.3500, 312.5000, 312.8000, 312.9000, 313.2500, 313.3000],
[312.5000, 312.8000, 312.9000, 313.2500, 313.3000, 313.6000],
[312.8000, 312.9000, 313.2500, 313.3000, 313.6000, 314.1000],
[312.9000, 313.2500, 313.3000, 313.6000, 314.1000, 314.1000],
[313.2500, 313.3000, 313.6000, 314.1000, 314.1000, 313.7500],
[313.3000, 313.6000, 314.1000, 314.1000, 313.7500, 313.7000],
[313.6000, 314.1000, 314.1000, 313.7500, 313.7000, 313.7500],
[314.1000, 314.1000, 313.7500, 313.7000, 313.7500, 313.6500],
[314.1000, 313.7500, 313.7000, 313.7500, 313.6500, 313.6000],
[313.7500, 313.7000, 313.7500, 313.6500, 313.6000, 313.4000],
[313.7000, 313.7500, 313.6500, 313.6000, 313.4000, 313.3000],
[313.7500, 313.6500, 313.6000, 313.4000, 313.3000, 313.3000],
[313.6500, 313.6000, 313.4000, 313.3000, 313.3000, 313.1000],
[313.6000, 313.4000, 313.3000, 313.3000, 313.1000, 313.0000],
[313.4000, 313.3000, 313.3000, 313.1000, 313.0000, 313.3000],
[313.3000, 313.3000, 313.1000, 313.0000, 313.3000, 313.3500],
[313.3000, 313.1000, 313.0000, 313.3000, 313.3500, 313.1500],
[313.1000, 313.0000, 313.3000, 313.3500, 313.1500, 313.0000],
[313.0000, 313.3000, 313.3500, 313.1500, 313.0000, 313.0000],
[313.3000, 313.3500, 313.1500, 313.0000, 313.0000, 312.9500],
[313.3500, 313.1500, 313.0000, 313.0000, 312.9500, 312.9000],
[313.1500, 313.0000, 313.0000, 312.9500, 312.9000, 312.7000],
[313.0000, 313.0000, 312.9500, 312.9000, 312.7000, 312.9500],
[313.0000, 312.9500, 312.9000, 312.7000, 312.9500, 312.9000],
[312.9500, 312.9000, 312.7000, 312.9500, 312.9000, 312.9000],
[312.9000, 312.7000, 312.9500, 312.9000, 312.9000, 312.8500],
[312.7000, 312.9500, 312.9000, 312.9000, 312.8500, 312.7500],
[312.9500, 312.9000, 312.9000, 312.8500, 312.7500, 312.8500],
[312.9000, 312.9000, 312.8500, 312.7500, 312.8500, 312.8500],
[312.9000, 312.8500, 312.7500, 312.8500, 312.8500, 312.8000],
[312.8500, 312.7500, 312.8500, 312.8500, 312.8000, 312.8000],
[312.7500, 312.8500, 312.8500, 312.8000, 312.8000, 312.7000],
[312.8500, 312.8500, 312.8000, 312.8000, 312.7000, 312.6000],
[312.8500, 312.8000, 312.8000, 312.7000, 312.6000, 312.7000],
[312.8000, 312.8000, 312.7000, 312.6000, 312.7000, 312.6000],
[312.8000, 312.7000, 312.6000, 312.7000, 312.6000, 312.6500],
[312.7000, 312.6000, 312.7000, 312.6000, 312.6500, 312.7500],
[312.6000, 312.7000, 312.6000, 312.6500, 312.7500, 312.6000],
[312.7000, 312.6000, 312.6500, 312.7500, 312.6000, 312.8500],
[312.6000, 312.6500, 312.7500, 312.6000, 312.8500, 312.8000],
[312.6500, 312.7500, 312.6000, 312.8500, 312.8000, 312.8000],
[312.7500, 312.6000, 312.8500, 312.8000, 312.8000, 312.6500],
[312.6000, 312.8500, 312.8000, 312.8000, 312.6500, 312.8000],
[312.8500, 312.8000, 312.8000, 312.6500, 312.8000, 312.7000],
[312.8000, 312.8000, 312.6500, 312.8000, 312.7000, 312.6500],
[312.8000, 312.6500, 312.8000, 312.7000, 312.6500, 312.7500],
[312.6500, 312.8000, 312.7000, 312.6500, 312.7500, 312.9000],
[312.8000, 312.7000, 312.6500, 312.7500, 312.9000, 312.8500],
[312.7000, 312.6500, 312.7500, 312.9000, 312.8500, 312.8000],
[312.6500, 312.7500, 312.9000, 312.8500, 312.8000, 312.8500],
[312.7500, 312.9000, 312.8500, 312.8000, 312.8500, 312.7500],
[312.9000, 312.8500, 312.8000, 312.8500, 312.7500, 312.8000],
[312.8500, 312.8000, 312.8500, 312.7500, 312.8000, 312.9000],
[312.8000, 312.8500, 312.7500, 312.8000, 312.9000, 312.9500],
[312.8500, 312.7500, 312.8000, 312.9000, 312.9500, 312.9500],
[312.7500, 312.8000, 312.9000, 312.9500, 312.9500, 313.0000],
[312.8000, 312.9000, 312.9500, 312.9500, 313.0000, 312.9500],
[312.9000, 312.9500, 312.9500, 313.0000, 312.9500, 312.9000],
[312.9500, 312.9500, 313.0000, 312.9500, 312.9000, 312.7500],
[312.9500, 313.0000, 312.9500, 312.9000, 312.7500, 313.0000],
[313.0000, 312.9500, 312.9000, 312.7500, 313.0000, 313.4000],
[312.9500, 312.9000, 312.7500, 313.0000, 313.4000, 313.1500],
[312.9000, 312.7500, 313.0000, 313.4000, 313.1500, 313.1500],
[312.7500, 313.0000, 313.4000, 313.1500, 313.1500, 313.0500],
[313.0000, 313.4000, 313.1500, 313.1500, 313.0500, 313.0500],
[313.4000, 313.1500, 313.1500, 313.0500, 313.0500, 313.1500],
[313.1500, 313.1500, 313.0500, 313.0500, 313.1500, 313.0000],
[313.1500, 313.0500, 313.0500, 313.1500, 313.0000, 313.0500],
[313.0500, 313.0500, 313.1500, 313.0000, 313.0500, 313.1000],
[313.0500, 313.1500, 313.0000, 313.0500, 313.1000, 313.0500],
[313.1500, 313.0000, 313.0500, 313.1000, 313.0500, 313.0000],
[313.0000, 313.0500, 313.1000, 313.0500, 313.0000, 313.0500],
[313.0500, 313.1000, 313.0500, 313.0000, 313.0500, 313.5500],
[313.1000, 313.0500, 313.0000, 313.0500, 313.5500, 313.4500],
[313.0500, 313.0000, 313.0500, 313.5500, 313.4500, 313.3000],
[313.0000, 313.0500, 313.5500, 313.4500, 313.3000, 313.3500],
[313.0500, 313.5500, 313.4500, 313.3000, 313.3500, 313.3500],
[313.5500, 313.4500, 313.3000, 313.3500, 313.3500, 313.1500],
[313.4500, 313.3000, 313.3500, 313.3500, 313.1500, 313.3000],
[313.3000, 313.3500, 313.3500, 313.1500, 313.3000, 313.3000],
[313.3500, 313.3500, 313.1500, 313.3000, 313.3000, 313.2500],
[313.3500, 313.1500, 313.3000, 313.3000, 313.2500, 313.2500],
[313.1500, 313.3000, 313.3000, 313.2500, 313.2500, 313.3000]]), 'decoder_lengths': tensor([1, 2, 3, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6])}
from pytorch-forecasting.
RaviKumarAndroid
commented on August 30, 2024
Also please try to fix the issue : #1 (comment)
from pytorch-forecasting.
jdb78
commented on August 30, 2024
Give it another try. I fixed a couple of issues including target normalization. Keep in mind that the strength of the algorithm is forecasting multiple timeseries.
from pytorch-forecasting.
jdb78
commented on August 30, 2024
I fixed some issues in 0.2.1 for GPU support.
from pytorch-forecasting.
jdb78
commented on August 30, 2024
Closing this as there were numerous fixed to the package - thanks again for reporting this issue! Please feel encouraged to raise a new issue in case you encounter any bugs.
from pytorch-forecasting.
Related Issues (20)
- AssertionError: filters should not remove entries all entries - check encoder/decoder lengths and lags
- Problem on TimeSeriesDataset
- Import error with latest version of numpy due to the `np.lib.function_base.iterable` HOT 1
- The only variable as input should be the target which is part of time_varying_unknown_reals HOT 1
- bug when `max_prediction_length=1` HOT 1
- Reduction for CrossEntropy is by default "none" and not alterable despite documentation
- Allow weight for Loss
- How to find the RMSE and MAE from raw predictions ? HOT 2
- [MNT] Is PyTorch-forecasting still under maintenance? HOT 2
- [ENH] option to turn off printing in prediction/inference HOT 3
- Truncated Backpropagation Through Time
- LSTM model in docs is fake
- How to extrapolate historic time series data into future with DeepAR model?
- Unexpected results for time-varying target variables.
- Question on using TFT without past target values
- how can this lib support the large data?
- Feature name unseen at fit time
- Issue with using optimise_hyperparameter with PyTorch DDP -> please help! :) HOT 1
- [ENH] un-stale the package and give simple instructions for working install HOT 4
- Understanding model.predict, decoder, test_dataset in TFT
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from pytorch-forecasting.