I am quite new to the field.
I am working on a problem involving time-series forecasting of single variable time-series. Data is collected from the pressure sensor on a patient in hospital.
Time window size is fixed to 6 time-points. the target variable is the pressure value at t_7. Rolling window is used
I have 2000 different time-series (each one referred to a different patient). Each time-series has a different length (let's say around 3000 measurements), because the observation time (the time during which the pressure sensor was recording) is different for every patient.
How can I train my LSTM model using all the 2000 time-series?