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I have 6 sequences, s1,..,s6. Using all sequences I want to predict a binary vector q = [0,0,0,1,1,1,0,0,0,1,1,1,...], which is a mask of the activity of the 6 sequences.

I have looked at seq2seq lstm models, but am struggling with the multiple-sequence-input and single-sequence-output architecture. Am I headed down the right path, or should I shift my focus to a convnet with 6 non-spatial dimensions, and 1 spatial dimension?

Thanks,

Ethan
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Zach LeFevre
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2 Answers2

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You will have to address a varying sequence length, one way or another. will likely have either perform some padding (e.g. using zeros to make all sequences equal to a max. sequence length).

Other approaches, e.g. used within NLP, to make training more efficient, are to splice series together (sentences in NLP), using a clear break/splitter (full-stops/periods in NLP).

Using a convolutional network sort of makes sense to me in your situation, predicting a binary output. As the convolutions will be measuring correlations in the input space, I can imagine the success of the model will be highly dependend on the nature of the problem. For some intuition of conv nets uses for sequences, have a look at this great introductory article. If each of your six sequences are inter-related, it allows the convolutions to pick up on those cross-correlations. If they are not at all related, I would proably first try Recurrent Networks (RNNs), such as the LSTM you mentioned.

Getting you head around the dimensions of a multi-variate LSTM can be daunting at first, but once you have addressed theissue of varying sequence length, it becomes a lot more manageable.

I don't know what framework you are using, but as an example in Keras/Tensorflow, the dimensions for you problem would be something like:

(batch_size, sequence_length, num_sequences)

batch_size can be set to None to give flexibility around your available hardware. sequence_length is where you need to decide on a length to use/create via padding/trimming etc. num_sequences = 6 :-)

If helpful, check out these threads, where I explained that stuff in more detail.

n1k31t4
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I suggest you transform your 6 sequences into 1 since they have the same lengths. You will have 1 sequence with more features for each timestamp.

It is hard to tell you which method would be better. But a LSTM model seems to be well suited for you.

Adrien D
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