[D] RNN for time varying covariates
I am currently working on a problem and I would like to ask for your advice on the best way to handle it.
So, the goal here is to predict the time of resolution of what we will call “incidents”. Namely, an incident is like an issue that is opened and needs to be fixed. The status of an incident varies with time – for instance, comments may be added or the priority may change from “Not important” to “Critical”.
My dataset looks like this : a row corresponds to the status of an incident at a given time. A single incident, identified by its ID, is then made of several rows. Some features do not change while others change every time there is a modification (see table below).
The idea would be to be able to give a prediction at any time in the life of an incident. I would like to make use of this sequential form to use recurrent neural networks, but I don’t know how to do it exactly.
There is a new row every time there is a change in status. So this means that the time between first and second row might be a day while the time between second and third row might be a week or a month.
I was thinking of treating the problem as a NLP problem, i.e. for a single ID, each row corresponds to one word (the embedded word). This would mean that the input to the RNN would be something like x = [x1, x2, …, xN] where xi = [xi1, …, xiM] a row of the dataset.
Would that make sense, and if not, how would you proceed ?
Thank you and have a nice day.