Identifying and Accounting for trend/seasonality in Predictor Variables I'm currently working with a dataset that has been collected over several years, and I suspect my predictor variables are changing over time for their predictive power. 

I could go back year by year and run the data the same way each time to see how efficient each predictor is, then trend the predictive power over time manually. There has to be a better way.

Can anyone point me towards the technique I should cram on?
Using time as an explanatory variable may be a good start. Then, depending on your model you may want to add interaction effects between time and your other variables. This will take into account change over time.

However, it will be a bit difficult to link evolution of the model to the evolution of some explanatory variable. If you know what variable evolve over time, you may just want to add time differentiated variables ($X_n - X_{n-1}$). This will give you a clearer view on what evolution is impacting the model output. Depending on your problem you might want to consider different time lags.