I can find examples of people using spectral analysis to break a time series into trigonometric components and then using those components with a multiple regression. Examples:
https://onlinecourses.science.psu.edu/stat501/node/364
Is time of the day (predictor in regression) a categorical or a continuous variable?
Does this extend to other forms of regression, like logistic regression? The outcome variable I'm working with is binary, but there's a pronounced periodic trend over time if it's summarized as a proportion. I was able to identify the periodic components and run a logistic model with the same kind of trig transformations seen in the above examples. The model predicts the actual proportions in the data really well, but I'm a bit leery of this approach since I literally can't find an example of someone doing this with a logistic regression.
Is there any reason I should avoid doing this? Have you seen any examples of people doing this before?