Food for thought: How can machine learning help better predict and understand changes in food prices?

TL;DR

This paper investigates how machine learning models can improve the prediction and understanding of food price fluctuations in Canada by evaluating various data-centric forecasting approaches.

Contribution

It systematically compares different forecasting models and data curation methods to enhance accuracy in predicting food price changes.

Findings

ML models improve prediction accuracy over traditional methods

Data curation significantly affects model performance

Different models vary in sensitivity to key factors

Abstract

In this work, we address a lack of systematic understanding of fluctuations in food affordability in Canada. Canada's Food Price Report (CPFR) is an annual publication that predicts food inflation over the next calendar year. The published predictions are a collaborative effort between forecasting teams that each employ their own approach at Canadian Universities: Dalhousie University, the University of British Columbia, the University of Saskatchewan, and the University of Guelph/Vector Institute. While the University of Guelph/Vector Institute forecasting team has leveraged machine learning (ML) in previous reports, the most recent editions (2024--2025) have also included a human-in-the-loop approach. For the 2025 report, this focus was expanded to evaluate several different data-centric approaches to improve forecast accuracy. In this study, we evaluate how different types of forecasting models perform when estimating food price fluctuations. We also examine the sensitivity of models that curate time series data representing key factors in food pricing.