I have time series data with variable sequence lengths. So something like:
date value label 2020-01-01 2 0 # first input time series 2020-01-02 1 0 # first input time series 2020-01-03 1 0 # first input time series 2020-01-01 3 1 # second input time series 2020-01-03 1 1 # second input time series
how is it possible to create a training dataset (numpy arrays) of shape
[samples, time_steps, n_features] when
time_steps is not consistent?
Additional Info: The model that is going to be trained is an
LSTM which is capable to handle variable input lengths.