Short-Term Fuzzy Forecasting of Brent Oil Prices
Bibliographic record
Abstract
Oil prices movements is very important macroeconomic factor for decision making. The accuracy of results fordifferent types of oil brands depends on models and algorithms. This paper evaluates the effectiveness of usingfuzzy sets to forecast daily Brent oil prices. It also contains possible modifications of the proposed method and incomparison with basic methods. The results suggest that Brent oil prices series have short memory because usinginformation about last 2-days prices shows better forecast accuracy. Forecasting based on fixed universe ofdiscourse shows better efficiency and it also proves that oil prices series has short memory. Adding theprobability of switching between linguistic terms in defuzzification function could be used to improve accuracyof predictions. Also the approach can take into consideration expert’s opinion about direction of future variation.The effective expert’s work can reduce errors of forecast from 1.5% till 0.76%. But this modification can be usedif experts correctly guess the direction of the change in trend in eight out of ten cases and more. The reasonableobtained results can be used by analysts dealing with the prediction of oil prices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".