Answers.Body: <p>Both the architecture have some use cases based on their architecture.</p>
  <p><strong>Transformer</strong></p>
  <ul>
  <li>Transformers are good when there are long-range dependencies, in those cases they outperform the LSTMs.</li>
  <li>Main key feature is High dimensional data in those cases self-attention is very useful.</li>
  <li>In terms of processing Transformer allows parallel processing during training which leads to faster training.</li>
  <li>In terms of feature interaction this model is very efficient in mapping complex feature relations.</li>
  </ul>
  <p><strong>LSTM</strong></p>
  <ul>
  <li>It is designed to deal with sequential data and catch dependencies effectively, only for shorter sequences.</li>
  <li>When you have limited data for training LSTM generalizes better because of fewer parameters than the Transformer.</li>
  <li>It is more suitable for online learning and real-time processing because it processes data sequentially.</li>
  <li>It is less demanding in terms of computation.</li>
  </ul>
  <p><strong>Main differences:</strong></p>
  <ul>
  <li><p><strong>Mechanism</strong>: 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.</p>
  </li>
  <li><p><strong>Memory</strong>: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.</p>
  </li>
  <li><p><strong>Complexity</strong>: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.</p>
  </li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-12-01T08:56:10.037Z
Answers.LastActivityDate: 2023-12-01T08:56:10.037Z
Answers.UserDisplayName: Hiren Namera
Body: <p>I’m working on a time-series classification problem and trying to decide whether to use a Transformer or an LSTM.</p>
  <p>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.</p>
  <p>But I’m unsure if that’s always the case.</p>
  <p>Are there specific use cases where Transformers are better than LSTMs for time-series classification?</p>
  <p>What are some key differences between the two architectures in representing time-series data?</p>
  
Tags: <classification><transformer><long-short-term-memory><time-series>
Title: When are Transformers better than LSTMs in time-series tasks such as classification?
UserDisplayName: Mark Cortejo
ThreadText: # When are Transformers better than LSTMs in time-series tasks such as classification?
  
  (Asked by: username_0 on 2023-12-01)
  
  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?
  
  **Tags:** classification, transformer, long-short-term-memory, time-series
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-12-01. Score: 2
  
  Both the architecture have some use cases based on their architecture.
  
  **Transformer**
  
  * 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.
  
  **LSTM**
  
  * 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.
  
  
  ---
  
  