Forecasting one time series with missing data with help of other time series I have time series $R$, which shows, how something changes at the regional level.

I have several time series $U_i$, which show, how something changes at a special unit $I$ level.

There are many units in the region. $R$ has no missing data. Different $U_i$ have their own missing periods. 

I want to forecast $U$ after a missing period using information of $R$ and information of $U$  when it was availible.

My thoghts till now:

Suppose $R$ is known on interval $[0, 365]$. Suppose $U_i$ is known on interval $[0,300]$. Let's take $R$ and $U_i$ both on interval $[0,300]$, take difference between them and trying to predict that difference with linear regression. So for interval $[301,365]$, I will have differences and to restore $U_i$, I will just have to take out my differences from $R$.

I don't like my solution, because:


We need a model for each $U_i$. 
Because, sometimes data is more sparse and I don't even have a $[0,300]$ known interval, so not able to train regression properly.
Not a fully complete answer, but some inputs.


Your time series are correlated. 
I assume that the measure you want to forecast for a region is an aggregation of units forecasts.


To address the first point, I usually use Vector Autoregressive Model (VAR) that forecast all time-series at once (each one being expressed as a regression using the others)
The second point involves the concept of hierarchical forecasting and reconciliation. You can exploit the fact that the regional forecast should/must equal the unit- forecasts. There can be a process to adjust forecasts to take that into account.

There are both packages for VaR and hierarchical reconciliation in R but as far as I know no direct code to handle both at the same time...

You may find this paper providing some details on the proposed approach:
https://mpra.ub.uni-muenchen.de/76556/1/MPRA_paper_76556.pdf