An integrated fuzzy mathematical programming-analysis of variance approach for forecasting gasoline consumption with ambiguous inputs: USA, Canada, Japan, Iran and Kuwait
Bibliographic record
Abstract
Gasoline as the most important vehicle’s fuel has a direct effect on economic development. In this study a fuzzy mathematical programming-analysis of variance approach is proposed to forecast gasoline consumption in the USA, Canada, Japan, Iran and Kuwait. The approach of this study utilises gross domestic production (GDP), annual population, number of vehicles and actual price of gasoline as the most standard independent variables. In this algorithm, gasoline consumption data from 1992 to 2005 for five mentioned countries are used to show its applicability. Proposed approach can select the best regression model between fuzzy and conventional methods for each country by means of analysis of variance (ANOVA), simultaneous Turkey test and mean absolute percentage error (MAPE). Results show that fuzzy regression provides better solution than conventional approaches. Moreover, it has more applicability toward gasoline consumption because it considers uncertainty and ambiguousness within the inputs and data sets. This is the first study that considers an integrated fuzzy mathematical programming-regression-ANOVA for gasoline consumption with uncertain inputs in both developed and developing countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".