# 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?

• How many labels are there (E.g.: 10s, 100s , 100s ). Dec 5 '18 at 14:03
• What is the granularity of timestamps (seconds, days, months...)? Answer to these questions might make some options feasible or not. Dec 5 '18 at 14:07
• around 200 labels, granularity is day (note a label doesn't show up every day. A new label could show up a week after the last one or the very next day) based on last 6 labels im trying to predict the 7th one. Dec 6 '18 at 6:29

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.