  Forecasting consumer price index (CPI) inflation is of paramount importance
for both academics and policymakers at the central banks. This study introduces
a filtered ensemble wavelet neural network (FEWNet) to forecast CPI inflation,
which is tested on BRIC countries. FEWNet breaks down inflation data into high
and low-frequency components using wavelets and utilizes them along with other
economic factors (economic policy uncertainty and geopolitical risk) to produce
forecasts. All the wavelet-transformed series and filtered exogenous variables
are fed into downstream autoregressive neural networks to make the final
ensemble forecast. Theoretically, we show that FEWNet reduces the empirical
risk compared to fully connected autoregressive neural networks. FEWNet is more
accurate than other forecasting methods and can also estimate the uncertainty
in its predictions due to its capacity to effectively capture non-linearities
and long-range dependencies in the data through its adaptable architecture.
This makes FEWNet a valuable tool for central banks to manage inflation.
