  A restricted Boltzmann machine (RBM) is a generative neural-network model
with many novel applications such as collaborative filtering and acoustic
modeling. An RBM lacks the capacity to retain memory, making it inappropriate
for dynamic data modeling as in time-series analysis. In this paper we address
this issue by proposing the p-RBM model, a generalization of the regular RBM
model, capable of retaining memory of p past states. We further show how to
train the p-RBM model using contrastive divergence and test our model on the
problem of predicting the stock market direction considering 100 stocks of the
NASDAQ-100 index. Obtained results show that the p-RBM offer promising
prediction potential.
