Ah okay! To be clear, let’s say I have daily samples:
['hi temp', 'inches rain', 'low temp']

Originally, I just made an autoencoder using nn.Linear along with ReLU activations, and reconstructed each day’s sample (all three variables). The anomaly detection was pretty poor with this approach.
So instead, I’m trying to think about it as three timeseries/sequences basically… I’m trying to learn how to reconstruct sequences using LSTMs instead, following this article: https://blog.floydhub.com/attention-mechanism/#bahdanau-att-step1
So in this case, I could use the nn.Embedding to as a way to have a dictionary of 3 separate embeddings, one for each of the 3 time-series?
Thank you for your help!