Forecasting Oil Prices: A Comparative Analysis between Neural Network and Regression Models
Bibliographic record
Abstract
After the war between Russia and Ukraine and its implications on various economies, energy security became a trending subject at the international level in 2022.Crude oil is an essential resource that plays a strategic role and its fluctuation has a major impact not only on the firms' profitability but also the stability of several countries.This research examines the possibility of forecasting oil prices where artificial neural network methods in addition to the multiple linear regressions were used in order to attempt to come out with a decent model that can help in forecasting oil prices.A comparison between these two models took place in order to choose the best one.The uniqueness of this research relied on the fact that 26 different variables were used all together, some of them were used for the first time in order to build the forecasting model.The period for constructing and testing both models extended from August 2006 to the beginning of 2019.The neural network model that was built showed to be more promising than the model that used multiple linear regression.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".