# Training a LSTM on a time serie containing multiple inputs for each timestep

I am trying to train a LSTM in order to use it for forecasting. The problem is basically a multivariate multi-step time series problem.

It is simply an experiment to see how statistical models (ARIMA, Holts-Winters, ...) and neural networks compare for a given problem.

As my dataset is perfectly fit for a statistical model, I am having trouble when trying to format it to train the LSTM as I have multiple entries for one timestep (corresponding to different entities) and I don't really know how to deal with it since the sequence is no longer tied by the time of observation. Let's say my dataset looks like the following example :

time | ent | obs

1 ---   1 ------      5

2 ---   1 ------     6

2 ---   5 ------     1

3 ---   2 ------     7

3 ---   5 ------     4


As you can see, not every entity have an entry for any given time, and one timestep can have multiple entries.

I thought of training the LSTM for each entity but I would have too few data for most of them. Some threads gave me the idea to separate each entity into batches but the number of observations is not constant so it wouldn't work for me.

How do you think I am supposed to tackle this problem?