The original datetime data provides year/month/day/hour information and I felt that year and month were probably the only necessary data. So I've currently modified the feature such that the data is represented only by year and month ( ex) March of 2016 would be 201603) and graphed each outcome with respect to the modified time variable consisting of year/month pair.
As you can see here, the 1st outcome has some minor seasonal fluctuations whereas the 3rd and 4th outcomes have clear seasonal trends. On the other hand, the 2nd outcome drastically decreases after May of 2015 (201505).
For my model prediction, I'd like to somehow incorporate time as a variable in a way that makes sense. What would be the best approach here? Can I just assume the earliest time period in the data to equal to 1 and increment by 1 for every month and treat the variable as a nominal category variable? Or something else?