I have a dataset where I have multiple entries for the same timestamp and I want to use LSTM to forecast the next timestamp given the previous 5 timesteps. From https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/ i understand how to deal with multivariate time series and in the blog he reshaped the time series such that we forecast the next step based on the previous time steps. But in my case i have multiple entries for the same time step when i do the reshape it creates the wrong relationship since it assumes the time it's sequential but in fact they are the same time step. In this scenario how should I structure my data for training?
The data is a COVID dataset with header below:
Accurate_Episode_Date, Age_Group, Client_Gender, Case_AcquisitionInfo, Reporting_PHU_City, Outbreak_Related, Outcome1 2020-03-30, 70s, MALE, OB, Stratford, Yes, Fatal