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`ForecastX` does not pass parameter to proba prediction methods of the inner forecaster.
It should also be checked why the tests did not capture this, as this results in the wrong column index (and…
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* Make sure to support probabilistic forecasts supported by xgboost
* it seems XGBoost also supports multi-outputs natively since version 1.6.2 - we should make sure we leverage this.
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## 🚀 Feature
Dear community,
As PyTorch Lightning mature, we believe it is important for the Lightning Team and its community to improve the Lightning onboarding process.
In that regards…
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Target date: end november 2023
wanted:
- packages:
- [x] Torch-2.1.1
- [x] numpy-1.26.2, with Array API v2022.12 support
- infrastructure :
- [x] a beta and partial Python-3.12 (mi…
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update by @fkiraly:
@patrickzib kept changing the original post constantly.
The most informative version was a link to this issue https://github.com/sktime/sktime/issues/3567
which had a brod…
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1. [Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes](https://proceedings.mlr.press/v162/benton22a/benton22a.pdf)
code [https://github.com/g-benton/V…
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**The problem:**
I'm looking to emit prediction intervals for each predicted value (the mean) in regression. I need that these intervals cover say 90% of true values and be as narrow as possible. In …
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How do I perform multiple output regression? Or is it simply not possible?
My current assumption is that I would have to modify the code-base such that XGMatrix supports a matrix as labels and that…
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hello! In the paper, you state you have a multivariate method, however as far as I understand each variate (or channel) is processed independently and the emission is also a point forecasting emission…
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**Describe the bug**
I have written software which individually uses either a CatBoost model, or a LightGBM model, each with quantile likelihoods. When I run historical_forecasts for either of these …