I want to fit a linear regression model to evaluate how the impact of an event on the dependent variable is influenced by the hour, day, and month of occurrence.
My plan of action is to apply one hot encoding to each of these three variables, creating a total of 43 features that will be used to fit the LR model (12 for each month, 7 for each week, 24 for each hour).
Having fit the model, I can then determine which of these features are significant, whether they have a negative/positive influence on the dependent variable, and rank them in order of the magnitude of their influence.
My questions are then as follows:
Does this seem like a sensible approach to those more experienced with such methods?
Will splitting each variable (hour, day, month) into so many granular categories be an issue due to the limited number of observations for each category?
Any input or advice on the above would be invaluable to my process.