Global gold prices forecasting using Bayesian nonparametric quantile generalized additive model
Bibliographic record
Abstract
Gold is one of the most attractive commodities and popular investments. Investment experts often recommend investing in gold because gold is one of the safest investments. It is a stable classic hedge, although the conditions of currency volatility or global markets are depreciated. However, the gold price fluctuations can be influenced by some other factors, such as the USD Index, which reflect and measure the strength of the US Dollar currency, and the Index of Dow Jones Industrial Average (DJIA) or a reflection of the political and economic conditions of the stock market. In this study, we conduct a global gold price forecast (USD) based on the USD Index, the DJIA Index, and the influence of time trends. Based on the data's characteristics, we face the fact that the data is nonlinear, contains outliers, and its pattern is not easy to specify parametrically. Due to the complexity of the model, we then propose a more flexible, robust modeling technique called the Bayesian Nonparametric Quantile Generalized Additive Model method. According to the results for the median case, the proposed method shows an accurate forecasting category due to the value of the Mean Absolute Percentage Error, MAPE less than 10 percent.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".