How to input data to LSTM? I have a labelled dataset (0 and 1) with two types of short length time series data (from day1-day5) as follows.

Type1 (Temperature at location1):

sensor1, 38, 38, 35, 33, 32
sensor2, 33, 32, 35, 36, 32
and so on ....


Type2 (Temperature at location2):

sensor1, 18, 18, 12, 11, 09
sensor2, 13, 12, 15, 16, 12
and so on ....


In summary my dataset looks as follows.

ID, time-series1, time-series2, label
sensor1, [38, 38, 35, 33, 32], [18, 18, 12, 11, 09], 0
sensor2, [33, 32, 35, 36, 32], [13, 12, 15, 16, 12], 1
and so on ....


I would like to use my dataset in LSTM (or a equivalent deep learning model).

However, I am still not clear how I can use my dataset in a deep learning model such as LSTM.

I am happy to provide more details if needed.
RNN input data must follow this pattern:


  ( Number of observations , Number of input series , Window size )


Keep the number of observations to None when you define the LSTM input_shape. The number of input series in this case is 2, since you are using two time series to make predictions. Window size is the length of your time series row. Your input shape would then be:

input_shape = (2, window_size)