<p>One solution is to use Multi-dimensional RNN or LSTM as described in <a href="https://arxiv.org/pdf/0705.2011.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/0705.2011.pdf</a>. In this case, your data will be treated as a sequence with 4 dimensions. This github repo provides an implementation of 2D LSTM <a href="https://github.com/philipperemy/tensorflow-multi-dimensional-lstm" rel="nofollow noreferrer">https://github.com/philipperemy/tensorflow-multi-dimensional-lstm</a>. Hope this helps</p>
Tensorflow: is it possible to create 2D LSTM?
<p>I have data that consists of 4 different time series, e.g.:</p>

<pre><code>    [35, 45, 47, 39...]
    [47, 60, 57, 55...]
    [42, 42, 61, 69...]
    [62, 70, 62, 65...]
</code></pre>

<p>Thing is, besides temporal dependency (horizontal one), there also exists vertical dependency (in columns, if we look at this example 'matrix').</p>

<p>Output vectors would be these same time series, only shifted for one step.</p>

<p>Is it possible to create LSTM network for each of time series (so, 4 networks in my case, and also 4 outputs) but also connect them vertically, i.e. create 2D LSTM?</p>

<p>If so, how would one achieve that in Tensorflow?</p>

<p>Is it also possible to make this kind of network deeper (have additional LSTM layers appended to each of these 4 networks)?</p>

<p>I hope I was clear enough with explanation.</p>