Intuitively, are both RNN and 1D conv nets more or less the same? I mean the input shape for both are 3-D tensors, with the shape of RNN being ( batch, timesteps, features) and the shape of 1D conv nets being (batch, steps, channels). They are both used for tasks involving sequences like time series, NLP etc. So my question here is this,
Are the steps and channels in 1D conv nets similar to the time steps and features in RNN? If they are, then why don't we use Conv 1D for time series problems instead of RNN since they are much faster compared to RNNs?
Please note that this is not a direct comparison, I know that they both work differently on an architectural level but I am just trying to get a high-level overview.