Food for thought: How can machine learning help better predict and understand changes in food prices?
This work addresses the problem of predicting food affordability changes for stakeholders in Canada, but it appears incremental as it builds on existing collaborative forecasting efforts.
The study evaluated different forecasting models, including machine learning and human-in-the-loop approaches, to predict food price fluctuations in Canada, aiming to improve the accuracy of the Canada's Food Price Report.
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.