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The research aims to compare econometric and machine learning approaches for predicting food inflation in Bangladesh.

January 20, 2026Scientific ReportsOpen Access

Benchmarking econometric, decomposable additive, and neural network methods for food inflation prediction featuring policy insights

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Comparative analysis forecasts food inflation in Bangladesh, suggesting improvements for energy markets amid crises.

The research aims to compare econometric and machine learning approaches for predicting food inflation in Bangladesh.
Analyzed food inflation data from July 2010 to March 2025.
Employed SARIMAX, TDANN, Prophet, and LSTM models for predictions.
Focused on the impact of climate variables and the Energy Price Index.
ANN model showed the lowest error metrics among tested methods.
Research supports nonlinear ML models as superior for predicting food inflation.
Findings provide actionable policy insights for managing energy markets and agricultural inputs.

A. Z. Javed (2026) studied this question.

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