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I know that for many applications a RNN (e.g. LSTM) needs a 3-dimensional input structure with [Batchsize, Sequence_Length, Features]. My question is if you also need a 3-dimensional input vector when making a "Point-To-Point" forecast, as classified here https://stackoverflow.com/questions/42334335/how-to-structure-an-lstm-neural-network-for-classification under "one to one"? In that case, the sequence length would just be 1 which would theoretically remove this dimension as far as I understand.

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Implementation-wise, the fact that the input and output sequence length is 1 is determined only by the data and does not affect the network architecture, so the accepted dimensionality of the input tensors is the same.

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  • $\begingroup$ Thanks Noe for your answer. Maybe one follow up question: What about the output dimensionality? Does a LSTM always output a 3-dimensional vector? As far as I understand it depends on wether you use return_sequences=True in the keras code and if you use a keras.layers.TimeDistributed. So if you use them, then the output is a 3-dimensional vector. If you don't use them, the output might also be 2-dimensional. Is that right and did I misunderstand something? $\endgroup$
    – PeterBe
    Dec 12, 2023 at 12:56
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    $\begingroup$ Yes, in case of return_sequences=True, Keras returns a 3D tensor, while with return_sequences=False it returns a 2D tensor. $\endgroup$
    – noe
    Dec 12, 2023 at 14:10

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