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 Autoencoder (or a equivalent deep learning model).

However, most of the tutorials I found online uses multivariate time-series data for LSTM AutoEncoder (e.g., https://towardsdatascience.com/step-by-step-understanding-lstm-autoencoder-layers-ffab055b6352).

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

I am happy to provide more details if needed.

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.