Forecasting inflation with a framework for model and neural architecture search with tree-structured search spaces.

This study automates the design of machine learning models for economic forecasting, with an application focus on Peru’s inflation. Such is achieved by employing an Automated Machine Learning (AutoML) framework that selects the best model configurations and data processing steps. This allows us to build models without manually trying out different options, saving time and potentially improving accuracy. The specific models explored are deep learning neural networks, which are machine learning models often used for complex forecasting tasks. We use two inflation forecasting schemes: one using a single model for headline inflation and another using two models one for food and energy inflation and another for inflation excluding food and energy, which are combined to predict inflation. By establishing this automated approach, we pave the way for further research on using machine learning to forecast economic data like inflation in Peru.