Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks

Oren Barkan, Jonathan Benchimol, Itamar Caspi, Eliya Cohen, Allon Hammer, Noam Koenigstein

Research output: Contribution to journalArticlepeer-review


We present a hierarchical architecture based on recurrent neural networks for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model, which utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Based on a large dataset from the US CPI-U index, our evaluations indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines. Our methodology and results provide additional forecasting measures and possibilities to policy and market makers on sectoral and component-specific price changes.

Original languageEnglish
JournalInternational Journal of Forecasting
StateAccepted/In press - 2022

Bibliographical note

Publisher Copyright:
© 2022 The Authors


  • Consumer Price Index
  • Disaggregated Inflation
  • Gated Recurrent Unit
  • Inflation Forecasting
  • Machine Learning
  • Recurrent Neural Networks


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