# Conditional restricted Boltzmann machines on a time series dataset

Preamble of the problem

I am currently trying to apply Conditional Restricted Boltzmann Machines on a time series dataset problem, in particular, the dataset constitutes of 10 day stock market recordings, and in each day, 50 stock value recordings are logged that are 5 minute apart from each other. Therefore, to my presumption, each day can be considered as a sequence {x0,x1,...,x50} and each recording in a day is a sample x.

My presumptions - please correct me if I'm wrong

1. Each day of recordings is a sequence

2. The purpose of C-RBM is to map a sequence - a day of stock recordings - into a set of features that can be used by any regression algorithm as input

3. A different RBM is trained on each sequence

Question

How do we feed the sample recordings of a sequence to an RBM ? Do we concatenate all the features of a sequence into one large vector and train the RBM on that? Or do we use each sequence as a miniature dataset on which the RBM trains, but then the time element is not emphasized here?

The question basically boils down to how, from an abstract point of view, can we use C-RBM to extract hidden features from sequences?

Your suggestions will be highly appreciated.

Thank you