angus924

Hi @StijnBr. Sorry, it has taken me a long time to return to this issue. Are you still having trouble with this? Currently we are limited in terms of what...

Ok, I see. Unfortunately, I don't have a great solution at present. I can think of two potential workarounds: (1) change [this line](https://github.com/alan-turing-institute/sktime/blob/93e0c402735016f31ad24baec86d49b5342eb3e4/sktime/transformations/panel/rocket/_minirocket.py#L82) so that instead of ignoring a `RandomState`...

Hi @bdudzik, good point, I'll add a note explaining the extension to multivariate. In the meantime, in summary, the multivariate version works by assigning a random subset of channels (up...

Hi @dfx1822375. Thanks for your message. I'm not 100% sure what the problem is from the information you have provided. It might be that something isn't quite right with the...

Hi @dfx1822375. The method won't necessarily work well on every dataset. If you could tell me a little bit more about your data (how many time series, time series length,...

Hi @dfx1822375, thanks very much for the additional information. Sorry for the slow response. Ok, so there are 7500 samples (1st dimension of the data). What are the 2nd and...

Thanks very much @dfx1822375. Thanks for the extra information and the plots, they are very useful. This is very interesting. I suppose, ideally, you might have kernels where the shape...

Hi @dfx1822375. You are quite right, that was my stupid mistake. You would need to get the mean and/or standard deviation per antenna *per sample*, so something like the following....

Hi @dfx1822375, sorry again for the delay. > And with 1D convolution, I understand the parameters as follows: torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True), you mean to use...

Hi @ShaowuChen, sorry for the very slow response. I would suggest either replacing the nan values with zeros, or do linear interpolation.