Alexander März

@yuezhihan I applied your approach to the M5 forecasting competition data, for the L-10 hierarchy. However, I see some flat forecasts that are not adaptive at all to the series...

@yuezhihan Kindly asking for an update on this. Thanks

I haven‘t pursued using the approach any further since I haven‘ received feedback from @yuezhihan. @ratheile Do you have some experience with using the approach in real out-of-sample evaluations?

@kwuking Even though I really like the approach of first learning time series representations in an self-supervised manner and then using a simple model for forecasting, I see some mixed...

@Geethen You might be interested in using [LightGBMLSS](https://github.com/StatMixedML/LightGBMLSS) which is an extension of LightGBM to probabilistic forecasting.

@leopiney Kindly asking for an update on this. Will the Siamese Deep Forest Code be released any time soon?

Is there any update on this? Can we make it a joint effort to have the multioutput regression available. Irrespective of the independence modelling of several responses/y-variables, it would be...

Based on the above discussion, I have extended the univariate [XGBoostLSS](https://github.com/StatMixedML/XGBoostLSS) to a multivariate framework called [Multi-Target XGBoostLSS Regression](https://arxiv.org/abs/2210.06831) that models multiple targets and their dependencies in a probabilistic regression...

@trivialfis Thanks for making the multi-output feature available in the fist place! Would be interested in your feedback, especially on how to improve the runtime for high-dimensional responses. Problem is...

@shubhashish1 It seems like that your frequency is misspecified, or that your date format is ambiguous and not aligned with the gluonts supported frequencies. Also note that, currently, gluonts only...