A Machine Learning Approach to Forecasting Consumer Food Prices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research thesis attempted to answer the following question, “What is the best way to predict food prices for the average Canadian consumer?” The overall objective was to forecast the Canada Consumer Price Index (CPI) to assess the performance of various machine learning techniques against three data models. Specifically, the research aimed to: 1. Determine the top performing model of the three models assessed (Holt-Winters, Food Price Report, Financial Futures-Market) 2. Determine the top performing machine learning technique of the four assessed (Linear Regression, Multilayer Perceptron, SMOreg, M5P Tree) 3. Evaluate the performance of the Multilayer Perceptron as the only technique to incorporate backpropagation. This research thesis was not meant to be a definitive approach to food price forecasts by favoring one technique over another but rather was intended to illustrate the accuracy of machine learning techniques in this forecasting domain by using the tools and techniques which can easily be duplicated by the average consumer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it