# Is it reasonable to do train/test splitting based upon information/entropy?

I want to divide my time series dataset into training and test sets. The data is seasonal and very noisy. When I randomly split, the test and train samples do not resemble in their distributions. Sometimes, train data get most of the noisy/peak points and sometimes these points go to test data. Can I split the data into train/test by calculating how much information the two sets hold. The information can be quantified by e.g. Shannon entropy or standard deviation etc. When I tried to search literature around this, I could not find any reference?

• If you are dealing with time series dataset, would it be nature that you split the data using a certain point on the time line (before which will be the training data, and after which will be the validation/test data)? If there are seasonal trends, you may want to make sure that your training data covers a full cycle. Jul 15, 2021 at 11:19