用机器学习分析加拿大食品价格波动，提升通胀预测准确性。
Food for thought: How can machine learning help better predict and understand changes in food prices?
- 对比多种数据驱动模型在食品价格预测中的表现。
- 发现时间序列数据处理对模型敏感性有显著影响。
- 适合关注农业经济与智能预测的政策研究者参考。
本文针对加拿大食品可负担性波动缺乏系统理解的问题展开研究。加拿大食品价格报告（CPFR）每年发布对未来一年食品通胀的预测，由多所大学的团队协作完成，包括达尔豪西大学、不列颠哥伦比亚大学、萨斯喀彻温大学以及圭尔夫大学/向量研究所。尽管圭尔夫大学/向量研究所团队此前已在报告中使用机器学习（ML），最近两版（2024–2025）还引入了人机协同方法。在2025年报告中，该团队进一步评估了多种以数据为中心的方法，以提升预测精度。本研究评估不同类型的预测模型在估算食品价格波动时的表现，并分析用于表示食品定价关键因素的时间序列数据的敏感性。
原文摘要 · Abstract (English)
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.
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