Predict time series data by group

Hello, I am studying machine learning. There are time series data as below.

time                   id         value
2010-04-11 23:00:00 2156213     10.0    
2010-04-12 02:00:00 2156213     3.0
2010-04-12 08:00:00 2156213     9.0
2010-02-01 04:00:00 2231441     0.0
2010-02-01 11:00:00 2431441     7.0
... ... ... ... ... ... ...
2016-10-13 08:00:00 2441232     134.0
2016-10-13 10:00:00 2441232     136.0
2016-10-13 14:00:00 2441232     140.0
2016-10-13 16:00:00 2441232     100.0
2016-10-14 04:00:00 2441232     120.0

After learning the time series data above, I want to predict the next value given the new id, time, and value. (The measured time, interval, and value change for each id are different.)

I thought of a way to combine the learned models after conducting regression learning separately for each ID, but there seems to be no way to combine them. I don't think there's an algorithm like that, but I don't know if I'm thinking too hard. Does anyone know any other way?