# Why can we not split train test data with 0.01 as parameter or 99% training data

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.

• We create a test dataset to know how better our model could perform on unseen data. You can use a 99-1 split but the model will be evaluated on less unseen samples. Sep 22 '19 at 0:59

If you want to be precise about it, you can analyse it statistically. Say you have a 0-1 classifier, and you expect the accuracy to be around $$\delta\in[0,1]$$, then it's not too hard to show that you need $$k=1/(\delta \epsilon^2)$$ test samples to measure you accuracy up to a factor $$1\pm\epsilon$$.
This means that if your amount of data is much larger than $$k$$, you can use much more uneven split. If you don't even have $$k$$ samples, then you're not going to get a precise result, and all you can do is consider methods such as cross validation.