I have time-series data of a few metrics. I know which metric is the response variable and the independent ones. I need to fit a model between them. The relationship could be linear, quadratic, logarithmic, piecewise linear, multiple linear, etc. Basically, it could be anything.
Is there any technique / properties I could use to find the relationship between the metrics and fit a model?
Right now, I have written a brute-force script in R.
For example, I have response variable A which depends on X1, X2 and X2.
A = C1*f1(X1)+C2*f2(X2)+C3*f3(X3) is my model.
My script tries all possible combinations of f1, f2 and f3.
By combinations, I mean that I initialy start with all linear, then one of them is quadratic, then cubic, then logarithmic, etc.
I am using lm().
I then choose the model which has the least AIC as my final model.
This obviously takes too long.
I definitely need an automated way of finding this model.
Could you suggest a better way to do this?