Lingrui Gan explains how to model for parameters whose effects change over time:
We can frame conversion prediction as a binary classification problem, with outcome “1” when the visitor converts, and outcome “0” when they do not. Suppose we build a model to predict conversion using site visitor features. Some examples of relevant features are: time of day, geographical features based on a visitor’s IP address, their device type, such as “iPhone”, and features extracted from paid ads the visitor interacted with online.
A static classification model, such as logistic regression, assumes the influence of all features is stable over time, in other words, the coefficients in the model are constants. For many applications, this assumption is reasonable—we wouldn’t expect huge variations in the effect of a visitor’s device type. In other situations, we may want to allow for coefficients that change over time—as we better optimize our paid ad channel, we expect features extracted from ad interactions to be more influential in our prediction model.
Read on for more.