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Hello everyone, I am trying to use an LSTM to predict and forecast the position of a vehicle and I would like to know how to train the system.

I have a dataset consisting of 230 vehicle samples i.e. a cell of 1 x 230 where each sample is a matrix of 4 features and the respective sequence length(60 - 300 timesteps). The objective is to forecast future (1 - 5 timesteps) steps of a given vehicle sample.

I am refering to this example to understand the way to forecast and this to see how to train the model for prediction. But in both the examples the LSTM model is used as a many to one example.

my features are in the x,y coordinate..

I would like to know how to train a LSTM model on multiple sequences containing mutltiple features and learn the behaviour of the vehicle model!

Thanks in advance

Asvin Kumar
on 30 Dec 2019

Edited: Asvin Kumar
on 30 Dec 2019

Have a look at the example here: https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.lstmlayer.html#d117e90134

Although this links to another example that uses the bilstmLayer, the underlying principles remain the same. You can use a fullyConnectedLayer with as many outputs as necessary for your use case. By setting the ‘OutputMode’ to ‘sequence’ in your lstmLayer and preparing the predictors as mentioned in the first example which you linked, you should be able to achieve your desired result.

In your case, the output size of the fullyConnectedLayer would be 4, I suppose. Your predictors would be shifted in time by 1-5 steps, whichever you're trying to forecast. It might make sense to drop the softmaxLayer and the classificationLayer from the example for your requirement.

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