I am trying to determine how to use machine learning models such as for eg., random Forest with (non-financial) time-series data.
Using an example, suppose we wanted to find based on monthly scores on subjects for each student how well he/she will do in a month-end exam that occurs every month.
Month 1 Data Name State EngScore MathScore HistScore ... MonthEndExamScore John NY 80 90 75 180 Jack TX 78 65 90 170 John CA 82 93 79 185
The same data is collected for the students in Month 2, 3 ... n. The task is to predict the MonthEndExam score of the current month using the student's historical data on performance during previous months.
For the current month, the scores on all the individual subjects are known by mid-month, whereas the MonthEndExam is not known until the end of the month and it is what we would like to predict.
I understand that one could use statistical methods such as ARIMA, etc, but I was wondering if there were similar methods in ML that can be applied here, ** in particular ** using packages in R (such as randomForest, caret, party, etc).
Thanks in advance.