Let's say that I would like to predict the temperature tomorrow. I could use the approach whereby I train a model based on a time-series dataset collected from a single location (for example, see this excellent walk-through:https://blogs.rstudio.com/tensorflow/posts/2017-12-20-time-series-forecasting-with-recurrent-neural-networks/).
However, let's say that I want to train a model that incorporated time-series from multiple weather recording sites. In this scenario, let us say that the recording observations from different sites are non-overlapping in time. Ideally, there would be a way to train models using non-consecutive observations (e.g. those collected from different sites), that also enabled us to quantify the influence of 'site' on our prediction.
Is this simply a case where one would train independent LSTM models for each site? Or are there alternative approaches whereby the total training data set can come from multiple observation sets (e.g. sites, non-consecutive observation blocks, etc.)?