  We present a hierarchical architecture based on Recurrent Neural Networks
(RNNs) for predicting disaggregated inflation components of the Consumer Price
Index (CPI). While the majority of existing research is focused mainly on
predicting the inflation headline, many economic and financial entities are
more interested in its partial disaggregated components. To this end, we
developed the novel Hierarchical Recurrent Neural Network (HRNN) model that
utilizes information from higher levels in the CPI hierarchy to improve
predictions at the more volatile lower levels. Our evaluations, based on a
large data-set from the US CPI-U index, indicate that the HRNN model
significantly outperforms a vast array of well-known inflation prediction
baselines.
