What you're describing is a linear model, it just has an ad hoc construction instead of something like a least squares training (https://en.wikipedia.org/wiki/Linear_model). Also linear models are about linear associations and work even with non-linear predictors. Your assumption in the model is explicitly a linear association so how the predictors vary shouldn't affect it.
I don't mean to disparage amateur statistics since often there are interesting things that experts miss that amateurs have insight into but this sort of data with few data points (the data you have doesn't sound independent) and a lot of confounding factors is not trivial to accurately model. Not that everyone needs to learn statistics but this sort of data (and also this topic) probably deserves a bit more of a rigorous approach.
As a very simple example, picking a time range for the model because the model doesn't work when you go farther back adds a lot of potential bias into the model and that along with the non-independence issue (and without looking at the data myself I don't know if there are other issues) are going to lead to overconfidence in conclusions.
I don't mean to disparage amateur statistics since often there are interesting things that experts miss that amateurs have insight into but this sort of data with few data points (the data you have doesn't sound independent) and a lot of confounding factors is not trivial to accurately model. Not that everyone needs to learn statistics but this sort of data (and also this topic) probably deserves a bit more of a rigorous approach.
As a very simple example, picking a time range for the model because the model doesn't work when you go farther back adds a lot of potential bias into the model and that along with the non-independence issue (and without looking at the data myself I don't know if there are other issues) are going to lead to overconfidence in conclusions.