the LSTM Autoencoder part preprocesses time series data from multiple DataFrames by normalizing and padding them to a uniform length, and then uses a sequence-to-sequence LSTM autoencoder for dimensionality reduction. The autoencoder, consisting of an LSTM-based encoder and decoder, is trained to reconstruct the input sequences, allowing the extraction of lower-dimensional embeddings from the encoder part,
the LSTM Autoencoder part preprocesses time series data from multiple DataFrames by normalizing and padding them to a uniform length, and then uses a sequence-to-sequence LSTM autoencoder for dimensionality reduction. The autoencoder, consisting of an LSTM-based encoder and decoder, is trained to reconstruct the input sequences, allowing the extraction of lower-dimensional embeddings from the encoder part,