Building a prodictive model in R based on historic data
i'm working on a daily data frames for one month, the main variables for each data frame, are like this
Date Function Presence
2015-09-02 08:01:28 Acce 1
2015-09-02 08:15:56 check-out 0
2015-09-02 08:16:23 Alarme 0
the idea is to learn over 15 days the habits of the owner in his home, the rate of his presence each time slot, and when he activate the alarme of the home, so after building this historic, we want to know (to predict) the next day (the 16th day), when he will activate his alarm based on the informations we calculated,
so basicly the historic should be transformed to a MODEL, but i cannot figure out how to do this ??
well what i have in hands are my inputs (i suppose) : the percentage of presence in the two half_hour before and after activating the alarm, and my input normally should be the time that the alarm should be activated, so what i have is like this
Presence 1st Time slot Presence 2nd Time slot Date
0.87 0 2015-09-02 08:16:23
0.91 0 2015-09-03 08:19:02
0.85 0 2015-09-04 08:18:11
i have the mean of the activated hour, of the percentage of presence in the two time slot
and every new day will be added to the historic (to the model, so the historic get bigger evry day by one day and the paramaters will change of course,teh mean, the max and the min of my indicators), it's like we are doing a "Statistical Learning"
So if you have any ideas, any clue that help me to start with, it would be helful for me cause when i serached, it's very vague for me, and i just need the right key to work :)