I am building a predictive model (binary target variable) in the financial services industry. One of the (many) potential predictors I am adding to the model is related to the customers checking account balance trend (longitudinal balance).
I'd like to capture if the balance is increasing or decreasing and how much. I have access to end of month balances going back a ways. One of the things I was considering is to, for each customer - fit a polynomial regression and include the coefficients into my predictive model.
In R, an example of a single customer:
balances <- c(657709,620729,713637,619224,558238,572402,536548,0,0,0) time <- seq(1:10) mod <- lm(balances~time+I(time*time)) mod$coefficients[2:3] mod$coefficients[2:3] time I(time * time) 61239.99 -13317.43
Thoughts? Of course the fit can be very poor, but as a global process to include into a predictive model does it have merit? Is there a better way?
It seems I have seen description of these coefficients in terms of velocity and acceleration, but I cant find it anywhere. Is this a true interpretation of them?