I recently read this post: Does it make sense to use a date variable in a regression?
Where the accepted answer says that dates can be used as regressors. So what I have done so far is the following: My start date is January 1, 2018, so this is day 0. January 2, 2018 is day 1 and so on, so December 31 is day 364. I also have data for 2019, so I made January 1, 2019 day 365, January 2, 2019 day 366 and so on. The thing is, I also have categorical variables that I converted to dummy variables using One Hot Encoding. So my data looks like this basically:
Day Feature1 Feature2 Feature3 0 0 1 0 1 1 0 0 2 0 1 0 3 0 0 1 ... 400 0 0 1
The question is: day is always increasing, so what I would think is that scaling so that it takes values between 0 and 1 (using MinMaxScaler in Python for example) would be a good idea. However, if I want to forecast what happens in day 401, how do I enter this value in the model that (I believe) is of the following form:
$y = \beta_0+\beta_1(Day)+\beta_2(Feature1)+\beta_3(Feature2)+\beta_4(Feature3)$
If Day is a number between 0 and 1, how will day be also a number between 0 and 1 if scaling only applies until day 400?