Most of the blogs mention about a good thumb rule to be 80-20 split for the train and test respectively.
My special case is a time series dataset and it is for the stock prices, which IMO is very time sensitive.
Question? Why can we not have a 99-1 train test split, for the model to learn all the information and time trends. Since my prediction will be in the future I will be ever increasing my test data set. I am using a neural network (rnn-lstm) for my prediction.
I am aware about cross_validation, froward_chaining which are better ways to train a time series dataset.