Comments (3)
@jjwow2 My understanding is time embedding has 6 dimensions, see this class:
from spacetimeformer.
Yeah the 6 is pretty arbitrary, it comes from dividing calendar dates into (Year, Month, Day, Hour, Minute, Second). Some datasets use all 6 values even when they don't need them (when data is always at daily intervals, for example). I think the traffic datasets are the only ones that are not in this format and that's just to get a fair comparison to prior work. IIRC Metr-LA and Pems-Bay just have hour, and day of the week variables.
from spacetimeformer.
Thank you! I was able to implement my own dataset successfully with this information but might be removing the seconds embedding in the future.
Overall the results are pretty good (.09 RMSE), but sometimes the pytorch lightning test method returns some non-sensical results, which I need to figure out.
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.