A Machine Learning Approach to Forecasting Consumer Food Prices

Abstract

Building on the success of the Canada Food Price Report 2017 and its inclusion of a machine learning methodology, this research thesis posed and attempted to answer the following question, “What is the best way to predict food prices for the average Canadian consumer?” The Canada Consumer Price Index (CPI) was selected as the dependent variable and forecasted against three data models to access their predictive values. The models included the popular Holt-Winters Triple Smoothing Exponent model as a benchmark, a financial futures-market data model and a model adapted from the Canada Food Price Report 2017. The hope was to create a more robust forecast model for future Canada Food Price Reports and similar econometric predictions. As hypothesized, the Financial Futures-Market based model outperformed the Food Price Report model with a 1.6% average error rate.