Time series prediction of discontinuous data In the context of time series prediction, I have read that time series is a series of data that taken at successive equally spaced points in time (which means its in order). What if I have a discontinuous time series data, for example:

If I have data that represnt a room temperature within the working hours, specifically from 7:00 am - 3:00 pm. And this data is repeated on the day of the week. So we have something similar to this: 

Saturday: [ Time series data from 7:00 am - 3:00 pm] 

Sunday: [ Time series data from 7:00 am - 3:00 pm] 

Monday: [ Time series data from 7:00 am - 12:00 pm]  -- Only up to 12 pm 

The first question:


Is that considered as time series data
Now if I want to predict the room temperature on Monday @ 1:00 pm. How can I do this using given this type of datasets?
Yes, it is time series with missing values. In fact you have process $X_t$, which is room temperature in moment $t$, but you have observations in specific time intervals. So your goal is to find out missing values between non-observed intervals.  There are several methods and packages for solving problems like this, here is an implementation of some methods in R: