In a machine learning context, what are the features and what the samples in a time series? I have a question which I have had for many years but never paid attention to it. When you have time series, in a machine learning context, what are the samples and the features? I would like to emphasize that I have time series, and that for this project, I am working with python (numpy arrays).
Perhaps I can explain a bit better. The network we are using asks for input data to be of shape input:(n_samples, n_features). If we fit only a single subject, whose time series has 150 time points, the network asks for the shape to be (150,1). Otherwise the networks throws an error because of input shape (understandable according to the documentation - link at the bottom).
Here comes the real question: If I hhave now 5 subjects with these  time series (i.e., a numpy array of shape (5,150)) what should I give to the network an array of (5,150) or (150,5). I think that the second one is more correct. If so, then how does it relate to the original question I posed: In a machine learning context, what are the features and what the samples in a time series?
I add the link for it may be helpful: https://pyrcn.readthedocs.io/en/main/api/pyrcn.echo_state_network.html

Together with the features, the series will be a multi-variate time series. Think of it like having different measurements of different things at the same time point (e.g. temperature, humidity, wind speed at time $t$ and bundling them as a vector signal). So, if we call a vector signal at time point $t$ as $\mathbf x_t$, value of this vector at each time point will be your sample point. And, each of the values inside the vector are called features. So, it is (150, 5) if you have 5 different entities you measure at each of the time instants.
