Domain Adaptation for Retail Demand Prediction
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".