Comments (3)
This indeed looks like a very cool possibility, thanks for putting this on our radar! This could be a subclass of our TorchForecastingModel
class. If you're up for the challenge, you can always consider giving this a shot yourself too (check out this post for guidelines regarding implementing a new model). For now we will put this on our backlog.
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This seems interesting to me as well! I am currently giving it a try (still working on it locally). I will let you know how it goes!
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Will be part of the next release!
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Related Issues (20)
- Mixed precision support for Torch Models
- `TFTModel.predict()` with `predict_likelihood_parameters=True` HOT 2
- Update examples
- Shap explanation HOT 2
- TFTExplainer: getting explanations when trained on multiple time series HOT 1
- Missing attribute when loading models trained in older darts version
- [BUG] TimeSeries.from_group_dataframe incompatible with integer timestamps
- [BUG] No activations in BlockRNNModel output MLP
- How to implement TIDE Model with similar lags to LGBM HOT 3
- [BUG] LinearRegression get_estimator method's required arguments appear to do nothing HOT 3
- [BUG] sklearn KNeighborsRegressor can't be fitted with darts RegressionModel HOT 2
- TiDE Model Stops At A Specific Epoch HOT 1
- Backtest with custom forecast dates HOT 2
- [Question] window_agg parameter not clear in anomaly scorers HOT 1
- [BUG] Training on multiple gpus HOT 1
- [BUG] How to prevent darts from caring about the date frequency HOT 2
- Question of validation data length and how samples are extracted from the validation dataset HOT 2
- [QUESTION] Behavior of TFT's .predict() method HOT 2
- [BUG]When the time_col parameter in TimeSeries.from_group_dataframe contains integers, it is still converted to a DatetimeIndex HOT 1
- [QUESTION] Simple question regarding backtest() in Darts. HOT 2
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