I have a dataset having 11k rows and 15 columns.
It’s a time series data, each row corresponds to one event in time, there could be multiple events in single day. Hence when creating data loader I did not use shuffle. I have a classification task with 7 classes. I used deep net for it and it didn’t give that good results and read that LSTM could be helpful as it has memory element. I wish to use LSTM in my model and hope that based on past data it captures the relationship and help me in predicting the classes better.
My question

what should be the seq length value in LSTM layer? Should it be 1? Corresponding to each row?
2)Also should I be initialising hidden state and cell state in each forward pass or use last computed hidden state and cell state value for next forward pass?
I read that weights are shared across timestep but since a batch of data is fed at a time how do these weights get shared?