Answers.Body: <p>It can depend on what you are learning to predict and what is your dataset.
  If I correctly understand your question I will assume that the training dataset is: ABCDEF -&gt; G</p>
  <p>And then you ask about prediction on: FEDCBA -&gt; G</p>
  <p>I would say yes. I does matter.</p>
  <p>Reference: <a href="https://colah.github.io/posts/2015-08-Understanding-LSTMs/" rel="nofollow noreferrer">colah.github.io</a> tutorial (or <a href="https://paperswithcode.com/paper/order-matters-sequence-to-sequence-for-sets" rel="nofollow noreferrer">PapersWithCode</a>), and their paper: <a href="https://arxiv.org/abs/1511.06391" rel="nofollow noreferrer">&quot;Order Matters: Sequence to sequence for sets&quot;, by Vinyals, Bengio, and Kudlur, (revised 23 Feb 2016)</a></p>
  <p>Where you can see that there is memory to remember the most important information. But the network is really sensitive to this type of data as it transforms it step by step. Not as a chunk in input</p>
  <p>After winter there is spring not the autumn.</p>
  <p>Noticed that the similar question was answered before at Cross Validated: <a href="https://stats.stackexchange.com/questions/301115/do-rnns-such-as-lstm-only-provide-value-for-sequences-where-order-matters">&quot;Do RNNs such as LSTM only provide value for sequences where order matters?&quot;</a></p>
  
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Answers.CreationDate: 2022-08-19T21:40:48.293Z
Answers.LastActivityDate: 2022-08-20T07:47:24.163Z
Answers.LastEditDate: 2022-08-20T07:47:24.163Z
Answers.UserDisplayName: Robert
Body: <p>In general, as long as the items are in order as per time sequence, does it matter if it's in ascending time sequence or descending time sequence for LSTM within the input vector?</p>
  <p>eg:
  ABCDEF -&gt; G</p>
  <p>BCDEFG -&gt; I</p>
  <p>vs</p>
  <p>FEDCBA -&gt; G</p>
  <p>GFEDCB -&gt; I</p>
  
Tags: <tensorflow><long-short-term-memory>
Title: Does sequence matter in LSTM?
UserDisplayName: Nibras Reeza
ThreadText: # Does sequence matter in LSTM?
  
  (Asked by: username_0 on 2022-08-18)
  
  In general, as long as the items are in order as per time sequence, does it matter if it's in ascending time sequence or descending time sequence for LSTM within the input vector?
  
  eg:
  ABCDEF -> G
  
  BCDEFG -> I
  
  vs
  
  FEDCBA -> G
  
  GFEDCB -> I
  
  **Tags:** tensorflow, long-short-term-memory
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-08-19. Score: 0 | Accepted answer
  
  It can depend on what you are learning to predict and what is your dataset.
  If I correctly understand your question I will assume that the training dataset is: ABCDEF -> G
  
  And then you ask about prediction on: FEDCBA -> G
  
  I would say yes. I does matter.
  
  Reference: [colah.github.io](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) tutorial (or [PapersWithCode](https://paperswithcode.com/paper/order-matters-sequence-to-sequence-for-sets)), and their paper: ["Order Matters: Sequence to sequence for sets", by Vinyals, Bengio, and Kudlur, (revised 23 Feb 2016)](https://arxiv.org/abs/1511.06391)
  
  Where you can see that there is memory to remember the most important information. But the network is really sensitive to this type of data as it transforms it step by step. Not as a chunk in input
  
  After winter there is spring not the autumn.
  
  Noticed that the similar question was answered before at Cross Validated: ["Do RNNs such as LSTM only provide value for sequences where order matters?"](https://stats.stackexchange.com/questions/301115/do-rnns-such-as-lstm-only-provide-value-for-sequences-where-order-matters)
  
  
  ---
  
  