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so I'm working on a Project where I want to predict the Vehicle Position from the Vehicle Data like speed, acceleration etc.. now the data that I have comes also with a timestamp for each sample ( I mean that I have also a timestamp feature).

at first I thought that I should get rid of that timestamp feature because it is not relevant to my Project, I mean logically, I will not need a timestamp feature to predict the vehicle position, that didn't make sense to me when I first took a look at the dataset. I thought other features like speed, acceleration, braking pressure etc.. are more important and I thought also that the solution for this Problem would be to use a normal Deep NN or RBFNN for making this Prediction. recently, I read some papers that shows how a Convolutional NN can be also used for regression and that confused me to choose the Architecture needed for my Project. this Week I also watched a Tutorial where a RNN/ LSTM was implemented for regression Tasks.

Now I'm very confused which architecture should I use for my Project. I also noticed that maybe if I used that timestamp feature, I can maybe use an RNN/LSTM Network for this Task but I don't know if my dataset can be seen as time-series dataset, actually the vehicle position doesn't depend on the time as far as I can tell.

Hopefully can someone answer me based on Experience. It would be also great to have some Papers or references where I can look for more.

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