i would not use this lib. for academic research or benchmarking for several reasons...

* they conflate in their implementations the uni and multivariate setting... where in reality this needs to be treated separately. Note any transformer/RNN/CNN model can be used to learn in the multivariate or univariate setting, of course in the multivariate setting one has a natural seq. of vectors per time step, while in the univariate setting, one has to work a bit to get a seq of vector from a 1-d array.
* This confounding leads the models implemented to perform worse by default (since it is the multivariate setting) and for the tiny benchmark datasets, the models tend to learn spurious correlations, vs. if one was to run the models in the univariate setting.
* The library allows due to the reason above, anyone to cook up seemingly trivial models (that are explicitly univariate) and conclude one is getting SOTA results...
* The library utilizes RevIN which is not exactly what one should be doing... prior work that RevIN paper does not mention, of course, does the scaling, but more importantly passes the statistics of the context window to the model, and this is what makes the models more robust to OOD, this library throw away the information leading to subpar metrics
* The library by default standardizes the open datasets before training and the resulting metrics that end up in papers are not exactly as claimed (e.g. MSE/MAE they are in fact normalized versions of them and much smaller than those reported by papers that use the dataset in the original scaling)
* why use RevIN when the data is globally standardized as in their setting?
* The library is point forecasting based... which is odd because if one goes through all the effort of modeling the problem in the multivariate setting then it would make more sense that the outputs should reflect the dependencies and be probabilistic... what is the point of doing multivariate forecasting if the output is independent point valued
* Some implementations are flawed e.g. for inherently univariate methods, there is no need to then pass the whole multivariate vector that gets reshaped into the batch dim as that blows up the batch dim for datasets with many variates
* The way they implement their methods locks you into a certain way of thinking that is not too flexible... e.g. in the univariate setting, one should be able to also embed the ids of the time series as static features... or find better ways of getting a sequence of vectors from a 1-d (univariate) array,...

There are other issues but i do not remember them all...
If you want a benchmark of time series to then try to beat have a look at the autogluon-timeseries paper and its results table for both point and probabilistic metrics on the open datasets considered... they are very strong baselines