Retail price time series imputation
Bibliographic record
Abstract
A new method to fill in, or impute, missing prices in retail price time series datasets is proposed, called retail price time series imputation (RPTSI). It is constructed from an ensemble of three existing methods: namely, price change lookup, central moving average, and polynomial interpolation. Four extended variations of RPTSI are also proposed by considering historical prices for similar products sold by the same retailer and equivalent products sold by competing retailers. Crowdsourced datasets from four North American cities over a year and a half period were used in experiments to evaluate the five RPTSI-based methods and to compare the results against those obtained using last value carried forward, mean imputation, moving average, polynomial interpolation, and multiple imputation. Accuracy was measured by using mean absolute imputation error. Experimental results showed that the RPTSI-based methods had significantly higher accuracy than the other methods on both univariate and multivariate time series datasets.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".