Identifying the Optimum Forecasting Horizon to Apply the Singular Spectrum Analysis on Daily Road Traffic Volume Forecasts
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
The paper delivers an assessment of Singular Spectrum Analysis (SSA) forecasting ability for short- and medium-term forecasting horizon, on real time traffic volume data. The key study goal is to estimate forecasting pertinency for daily traffic volume, based upon measurements at toll station. The suggested methodology is tested on real data from Moschohorion and Pelasgia Toll Station – Greece, utilizing custom developed forecasting software toolbox. Applied research results confirm an advanced forecasting ability of proposed methodology for short-term forecasting horizon against medium term forecasting horizon, when performance is compared upon the statistical criteria of the coefficient of determination R2. The obtained results present that SSA forecasting model could provide a competent forecasting methodology for road traffic volume data.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it