From what I know, Recurrent NNs perform very well in case of sequential data. However, I have also read at many places that it can be used for non-sequential data as well. For instance in the article 'The unreasonable effectiveness of Recurrent Neural Networks' by Andrej Karpathy:
Sequential processing in absence of sequences
You might be thinking that having sequences as inputs or outputs could be relatively rare, but an important point to realize is that even if your inputs/outputs are fixed vectors, it is still possible to use this powerful formalism to process them in a sequential manner.
From what I can make out, we model our data in a sequential form. So, if the input is a data point, the output would be the data point after
t time steps, and we train our model on that data.
I came across a Kaggle problem and was wondering if RNNs can be applied to it. The problem is to classify credit card frauds. The only thing which can be modelled as a sequence, I believe, is the class of the output but then wouldn't the model be just learning the sequence of zeros and ones based on the other features? Because there is nothing which identifies a previous datapoint and can be used to learn a sequence from. So, does that mean RNNs cannot be applied to this problem because it lacks that ability to be converted into sequential form, or maybe I am missing something?