is it a good idea to use k-fold cross-validation in the recurrent neural network (RNN) to alleviate overfitting?
- A potential solution could be
L2 / Dropout Regularizationbut it might kill RNN performance as discussed here. This solution can affect the ability of RNNs to learn and retain information for longer time.
- My dataset is strictly based on time series i.e
auto-correlated with time and depends on the order of events. With standard k-fold cross-validation, it leaves out some part of the data, trains the model on the rest while deteriorating the time-series order. What can be an alternate solution?