I've been reading about one-hot encoding for categorical values. Could similar mechanism make sense for continuous values? I'm looking at a problem, where I try to predict the volume of product X, with a variables being the price of product Y.
The reason is to increase the strength of the signal from the price of Y, which otherwise could be understated because overall prices fluctuate in relatively low range. The idea would be to e.g. remove attribute of "Price Y" and replace it with "Promotion Y" based on a set threshold.
Price X | Price Y | Vol X | Vol Y | Promotion X | Promotion Y 100 | 90 | 40 | 50 | 0 | 0 75 | 75 | 60 | 60 | 1 | 1 100 | 75 | 30 | 70 | 0 | 0
I could just test it out on a dataset, but this could be very specific to this very set and I just want some more theoretical suggestion if and where such approaches would make sense?