Multiple time period imputation technique for multiple missing traffic variables: nonparametric regression approach
Bibliographic record
Abstract
The objective of this study is to introduce an effective and practical model, based on non-parametric regression, to instantaneously estimate multivariate imputations replacing multiple missing variables during multiple time periods. The developed model was essentially designed for system-oriented, real-world applications. In an empirical study with real-world data, the proposed model, on the whole, outperformed the seasonal auto-regressive integrated moving average (ARIMA). The analysis of the results indicates that the introduced model was more applicable to multivariate imputation during multiple time intervals than that of ARIMA. In addition, it was revealed that ARIMA could somewhat deform the relationship between the volume (q) and speed (s), whereas the developed model reproduced the q–s relationship more similarly than ARIMA. Moreover, the proposed model is very simple and does not require system operators to input or recalibrate any external parameters because it was developed for applications of real data management systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".