Domain Adaptation for Retail Demand Prediction
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
Predicting the demand of products in the retail industry is a complex task, especially when there are changes in the market. This study examines three such changes in the retail industry: the COVID-19 pandemic, opening a new store, and introducing a new product. The study found that the accuracy of demand prediction models decreases after these changes. To address this, the researchers used domain adaptation methods, such as Frustratingly Easy and Kernel Mean Matching, to improve the accuracy of predictions by utilizing data from before the changes and adapting it to the data after the changes. The study also found that using a pairing technique can further enhance prediction accuracy. Two forecasting models, XGBoost and Transformers, were used in the study and XGBoost was found to be more effective. The study used point-of-sale data from 89 locations of Alimentation Couche-Tard convenience stores in Montreal between 2019-07 and 2021-02, and considered product prices alongside sales data to predict product demand. The focus of the study is on the two best-selling product categories of coffee and energy drinks.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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