Comments (1)
There are some pytorch_lightning based ways to do this but honestly I think the least confusing way is to use the trained model as a regular pytorch module. I added a predict
function to Forecaster
that handles making the unknown y_t
(target sequence variable values) and dealing with the distributional output of spacetimeformer. It takes a batch of x_c, y_c, x_t
tensors (with y_c un-normalized), then returns un-normalized predictions of y_t.
from spacetimeformer.
Related Issues (20)
- AttributeError: module 'tqdm' has no attribute 'auto' HOT 8
- how should I find the datas for example
- Colab installation
- Random seed
- TypeError: accuracy() missing 1 required positional argument: 'task' HOT 1
- MisconfigurationException: `configure_optimizers` must include a monitor when a `ReduceLROnPlateau` scheduler is used HOT 5
- Training with custom dataset. Error: object is not callable HOT 2
- colab
- Pip installation
- Error while resuming training from saved checkpoint
- Huggingface Integration HOT 1
- Memory requirements to replicate on Pems-Bay HOT 2
- Working with custom dataset, IndexError: index out of range in self HOT 1
- ValueError: SyncBatchNorm layers only work with GPU modules HOT 2
- prediction vs labels
- Unable to reproduce results for spacetimeformer HOT 2
- `configure_optimizers` must include a monitor when a `ReduceLROnPlateau` scheduler is used. For example: {"optimizer": optimizer, "lr_scheduler": scheduler, "monitor": "metric_to_track"} HOT 2
- Question regarding the use of projected exogenous variable values
- using own dataset
- Publication of Training Commands
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from spacetimeformer.