I have time series data including sevaral days.
I try to predict a grade of tomorrow, which is range from 0 to 100.
And I assume that this grade depends on 3 time-series independent features.
Every each day is a unit and has a grade, but each unit(day) has different length of time series data.
Because each day has lots of data length, I rolled window, which size is 1080 and stride is 180, on each day.
So I tried to transform it into (# of unit(sample), # of window, windowsize, features).
Finally I get (450, # window(not fixed), 1080, 3).
450 days, Each day has same length window(1080) and features(3), but Number of windows are different.
I condisered zero-padding, but I heard that I can use dynamic(?) LSTM with differnent length of input.
The problem is that shape of input of Keras LSTM must be 3 dimension.
So I'm confused how I should fit this shape to LSTM model.
My goal is that when I put a new day, which shape is (# of window, 1080, 3) into LSTM model, I want it to predict a grade.
Could you give me any suggestions or advices? Thank you.