Consider a stock that values
- 10,00 USD in 2010
- 75,00 USD in 2015
- 150,00 USD in 2020
- and it continues to grow by this day.
decision tree based algorithms like
xgboost are generating the tree (splitting the values) based on the ranges, I don’t understand how the tree built on the past data (e.g. years 2000 - 2015) could be in any form applicable for the future price predictions (e.g. years 2015 - 2080).
Could somebody confirm that that feature normalization is truly not required for data that grows beyond the original(/fit/train) range with time?
Do I need to run the raw stock price through some log or sigmoid function before training or is
xgboost actually smart enough to deal with this kind of data automatically?