<p>No, it just has to be a sequence like requirement.</p>
<p><a href="https://arxiv.org/abs/1503.04069" rel="nofollow noreferrer">Klaus Greff, et al., LSTM: A Search Space Odyssey, 2015</a> :
Since LSTMs are effective at capturing long-term temporal dependencies without suffering from the optimization hurdles that plague simple recurrent networks (SRNs), they have been used to advance the state of the art for many difficult problems. This includes handwriting recognition and generation, language modeling and translation, acoustic modeling of speech, speech synthesis, protein secondary structure prediction, analysis of audio, and video data among others.</p>
<p><a href="https://www.mitpressjournals.org/doi/abs/10.1162/089976600300015015" rel="nofollow noreferrer">Felix A. Gers, et al., Learning to Forget: Continual Prediction with LSTM, 2000</a> : LSTM holds promise for any sequential processing task in which we suspect that a hierarchical decomposition may exist, but do not know in advance what this decomposition is.</p>
Is Seq2Seq Models used for Time series only?
<p>Can we use Seq2Seq model with input data that has no temporal relation ( not a time series )? For example I have a list of image regions that  I would like to feed my seq2seq model. And the the model should predict an description ( output is time series |) or captions.</p>
<p>I’m not asking from the technical perspective, I know that if the data is in the correct format then I can do that. My question is rather theoretical, is it ok to use Seq2Seq with none time series data? And are there any papers/articles/references of using Seq2Seq in this setting ?</p>