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Suppose I want to train a RBM (or even a DBN architecture) and then fine-tune the parameter training a Feedforward NN. In my case the dataset is composed of time series, so in principle there is a temporal dependency between observation.

I use to apply ML techniques which involve LSTM module, so when it comes to split the dataset into the training and the test part this leads to divide it looking at the timeline.

However, It seems that a simple RBM (but also a MLP) is not able to capture the temporal dependencies and the sample are provided as input one at a time. Is therefore reasonable to shuffle a dataset composed of sequences, as it is usually done for other kind of data, as images?

I have a looked for some practical reference about it, but even Hinton's guide gives no idea about this problem.

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It doesn't make much sense to feed one item of a time sequence at a time into an RBM model, if you're trying to model sequences -- you should be feeding in entire sequences at a time, in which case temporal dependencies will be captured.

Is therefore reasonable to shuffle a dataset composed of sequences, as it is usually done for other kind of data, as images?

It's perfectly fine to shuffle a dataset of sequences, so long as you don't unintentionally break up any sequences in the shuffling process.

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    $\begingroup$ Maybe my explanation was not so clear. I perfectly understand that It does not make sense to break a sequence. I was talking about time series, in my case financial time series. For my understanding, the RBM accepts one row of the dataset at a time, which means every timestep enters independently from the other into the model. From here my doubt about shuffling or not the dataset. The RBMs are not LSTMs structure in which you can feed a sequence of timsteps and capture the time dependency between observations. $\endgroup$ – Alexbrini Jun 3 at 9:14

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