Bohdan Bilonoh
Bohdan Bilonoh
Hello! Any updates on it?
Hi there 👋 I am also interested in this question
Hello! Got the same error. I fixed it by upgrading to scikit-learn version 1.1.3
Hi! There is an idea to make the weights part of the `TimeSeries` class as an attribute for xarray (like a static covs or a hierarchy). I could contribute if...
Sounds interesting. My motivation was to make the sample weights part of the input and use them as `weight_cols` for `TimeSeries.from_dataframe`. This could allow all slicing logic to be hidden...
It will be interesting to see the code of the new logic. Very simple example: E-commerce time series that contain revenue and margin as targets and have to be predicted...
My vision of the sample weights was similar to the weights passed to `Likelihood.compute_loss` and in this scenario sample and/or timestamp and/or component could be weighted
Faced the same issue. Found that distributed Lighting inference requires `BasePredictionWriter` https://lightning.ai/docs/pytorch/stable/deploy/production_basic.html#enable-distributed-inference
@dennisbader sure. As a hot fix I changed: `TorchForecastingModel.predict` Original ```python return predictions[0] if called_with_single_series else predictions ``` New code ```python if predictions: return predictions[0] if called_with_single_series else predictions else:...