Automated Box–Jenkins forecasting tool with an application for passenger demand in urban rail systems
Why this work is in the frame
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Bibliographic record
Abstract
Summary Efficient management of public transportation systems is one of the most important requirements in rapidly urbanizing world. Forecasting the demand for transportation is critical in planning and scheduling efficient operations by transportation systems managers. In this paper, a time series forecasting framework based on Box–Jenkins method is developed for public transportation systems. We present a framework that is comprehensive, automated, accurate, and fast. Moreover, it is applicable to any time series forecasting problem regardless of the application sector. It substitutes the human judgment with a combination of statistical tests, simplifies the time‐consuming model selection part with enumeration, and it applies a number of comprehensive tests to select an accurate model. We implemented all steps of the proposed framework in MATLAB as a comprehensive forecasting tool. We tested our model on real passenger traffic data from Istanbul Metro. The numerical tests show the proposed framework is very effective and gives higher accuracy than the other models that have been used in many studies in the literature. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it