I’m working on a time-series classification problem and trying to decide whether to use a Transformer or an LSTM.

From what I’ve learned, Transformers are better suited for capturing long-range dependencies in time-series data vs LSTM. The transformers are an excellent option for processing word data for Natural Language tasks, and frameworks are currently built to represent time-series data, such as TabTransformer and TimeSeriesTransformer models.

But I’m unsure if that’s always the case.

Are there specific use cases where Transformers are better than LSTMs for time-series classification?

What are some key differences between the two architectures in representing time-series data?


1 Answer 1


Both the architecture have some use cases based on their architecture.


  • Transformers are good when there are long-range dependencies, in those cases they outperform the LSTMs.
  • Main key feature is High dimensional data in those cases self-attention is very useful.
  • In terms of processing Transformer allows parallel processing during training which leads to faster training.
  • In terms of feature interaction this model is very efficient in mapping complex feature relations.


  • It is designed to deal with sequential data and catch dependencies effectively, only for shorter sequences.
  • When you have limited data for training LSTM generalizes better because of fewer parameters than the Transformer.
  • It is more suitable for online learning and real-time processing because it processes data sequentially.
  • It is less demanding in terms of computation.

Main differences:

  • Mechanism: The Transformer uses attention to weigh the importance of different parts of the input, which helps to learn global dependencies, while LSTM processes data sequentially and uses a gate mechanism for informal flow control, which struggles in long sequences.

  • Memory:LSTM has an inbuilt memory cell state that carries information, which helps in time-series-based patterns while Transformer doesn't have recursive memory but has positional encoding to understand sequence order.

  • Complexity:Transformer has a high number of params because of self-attention head and FFN (feed-forward network) while LSTM has fewer params which is beneficial in case of limited data.


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