I have a dataset of airfare price tickets that were scraped throughout a 6 month period where each observation represents a particular price for a specific flight on a specific date that it was scraped. In other words, a specific unique flight may appear multiple times in the dataset if it's scraped multiple times on different days. For example,
Scrape Date: 11/13/19, Days To Trip: 42, Flight: DL1345 , Departure: 12/25, Time: 5:00PM, Price: 290 Scrape Date: 11/22/19, Days To Trip: 33, Flight: DL1345 , Departure: 12/25, Time: 5:00PM, Price: 330 Scrape Date: 12/01/19, Days To Trip: 24, Flight: DL1345 , Departure: 12/25, Time: 5:00PM, Price: 349
I know that with time-series data such as stock prices, you want to split your training/testing data so that the data in testing is in the future and comes after the data in training. However, I don't believe the dataset I have would warrant a split like this and I can instead randomly shuffle the data for train/test split but I am not 100% sure on the right call. Should I split the data based on time or can I randomly sample since the price of the tickets don't depend on each other?