I am working on a multinomial machine learning algorithm that labels stocks with buy/sell signals. My code updates with the most recent quantitative data about the stocks daily, so obviously the data changes daily and each time I run the code. My problem is that each stock is an observation, and for ML purposes the data set I am left with is very small (<10000 obs). I have fiddled with various ways to increase the sample size - but each way has been extremely labor intensive. I have wondered if since the data changes daily, the predictions will also change daily, however slightly (being multinomial gives more levels and maximizes variety). If I save each generated daily data set, can I stack them over time and add them to the latest generated dataset? That is, is it alright to ignore change in time when making a dataset - or is it more complex than that?