Hi,
You are right. Convolution operation works on spatial/temporal data (in our examples) and you can think of your data in this way, that you have 5 features for each time stamp, not 5 time staps for each feature.
A better way is to assume that your input data is the output of another operation, in this case, 5 features corresponds to 5 different kernels that recorded features for all timestamps. When you are defining conv layer, you need to specify output channel which eactly could have the same definition as 5 features.
I do not know you are familiar with image or not, but for a 2D image (1D can be true to but does not make much sense), then shape of input would be [number of images, channel, height, width] which for 1 RGB image would be [1, 3, 10, 10]. 3 corresponds to Red, Green and Blue, same as your 5 features. Then 10x10 is the spatial domain, which in your case, it is a 1D temporal domain. So, it could be like [1, 3, 10].
You can transpose the dimensions using tensor.permute().
Bests