Fuel Sales Forecasting with SARIMA-GARCH and Rolling Window
Bibliographic record
Abstract
This research article proposes an innovative strategy to improve long-term forecasting accuracy for gasoline sales in Canada. The SARIMA-GARCH model was used with the rolling window forecasting technique to successfully address varying seasons, changing patterns, and conditional variance on the historical data of gasoline sales in Canada (1993-01-01 to 2015-12-01) with the sample size of 276. The rolling window forecasting technique was used to forecast one-step-ahead value and update the model to fresh observations while minimizing look-back bias and attaining good long-term forecasting accuracy. The findings revealed considerable improvements in forecasting accuracy. The proposed SARIMA-GARCH model with rolling window forecasting produced a RMSE of 151026.28 and a Mean Absolute Percentage Error (MAPE) of 0.0340. This outperformed other baseline models, including simple SARIMA model which had a RMSE of 329,689.88 and a MAPE of 0.0786, and the GARCH model which had a RMSE of 316,168.33 and a MAPE of 0.0685. The data shows that the proposed approach is effective for accurate long-term forecasting of gasoline sales in Canada. The study provides significant data for politicians, industry professionals, and energy investors, assisting them in making informed decisions about resource allocation, strategic planning, and risk management.
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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 | 0.002 | 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 | 0.000 |
| 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".