I've been working with MLP's for a while. Whenever I assumed that the past values of a feature might be useful for predicting the future values of Y, I would just create a new column in my data frame with Feature(t-1). This process would be repeated for further lags t-2,t-3...t-n.
Besides the obvious problem of the curse of dimensionality, I am worried that the MLP doesn't know how to weight those time lagged features that are now in a new separate column.
So in a nutshell:
Is the above approach wrong?
How does an RNN solve this?