Bibliographic record
Abstract
The movements in oil prices are very complex and, therefore, seem to be unpredictable. However, one of the main challenges facing econometric models is to forecast such seemingly unpredictable economic series. Traditional linear structural models have not been promising when used for oil price forecasting. Although linear and nonlinear time series models have performed much better in forecasting oil prices, there is still room for improvement. If the data generating process is nonlinear, applying linear models could result in large forecast errors. Model specification in nonlinear modeling, however, can be very case dependent and time-consuming. In this paper, we model and forecast daily crude oil futures prices from 1983 to 2003, listed in NYMEX, applying ARIMA and GARCH models. We then test for chaos using embedding dimension, BDS(L), Lyapunov exponent, and neural networks tests. Finally, we set up a nonlinear and flexible ANN model to forecast the series. Since the test results indicate that crude oil futures prices follow a complex nonlinear dynamic process, we expect that the ANN model will improve forecasting accuracy. A comparison of the results of the forecasts among different models confirms that this is indeed the case.
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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 | 0.003 |
| 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) | 0.001 | 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".