Input and output Dimension of LSTM RNN I am fairly new to RNNs and Im having trouble setting up the desired output from RNN using Keras library. Each datapoint in my dataset consist of a pattern of labels and timestamp of occurrence of each label and based on the pattern of label I want to predict what the next label in the pattern be. I have developed the model which takes series of label as input and outputs the next label in the series but now I want to input labels as well as their timestamp and receive output the next label as well as the timestamp of its occurrence. How can I achieve this with a single model?
You can convert input to vectors of labels + day number . E.g: 

(First column is day-number, rest of the columns indicate presence/absence of a label)


[[ 1 0 1 0 0 0 0 .......]
 [ 2 1 0 0 1 0 0 .......]
 [ 3 1 1 1 1 0 0 .......]
 [ 4 1 0 1 1 0 0 .......]
 [ 5 1 1 0 1 0 0 .......]]

(5000, 201)



Output should be a vector of probabilities for individual labels.

This article has an example :

https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/