I'm trying to decide whether I should scale my features & responses for training, and I'm in a situation where I can't just try both scaling and not scaling.
My features currently have an std around 0.05, and the behavior of the timeseries I'm studying is very much scale dependent (0.5 means a VERY different thing than a 0.05 in terms of what's happening in the market).
Can I expect major improvements in performance, optimizer-wise, if I scaled my features to have std of 1?
My models are different combinations of MLP and 1d conv, and my algorithm is gradient descent with the Adam optimizer.
Thank you! [and yes; I'm another one of those people who are trying to forecast the stock market]
Louis is right that I should try both. But I'm leaving the question up just in case someone comes along and gives a mathematical proof of exactly normalization is useful /useless.