Estimation of International Gold Price by Fusing Deep/Shallow Machine Learning
Post-publication record
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Bibliographic record
Abstract
In this work, we propose a new method that combines the support vector machine (SVM) and the long short-term memory (LSTM) model utilizing the theory of quotient space to predict the price of gold by leveraging the price factors that have supposedly an impact on the gold price. The Pearson correlation coefficient is employed to measure the relations between nine price factors and gold price. The five price factors with larger correlation coefficients are picked. Then, by utilizing the Granger causality test, the gold price may change concerning the two price factors when time is a concern, which results in combining the results of the correlation analysis with the results of Granger causality leading to a total of seven price factors. Also, the gold price can be divided into the quarters of the year according to the theory of the quotient space and temporal attribute. With three granularities per month, a 3-layer quotient space is constructed based on the synthesized and calculated granularities. The proposed method provides the prediction results that are compared with the predicted values of some grey models (GM) and the actual gold price, respectively. The results suggested that the prediction results of gold price have a comparable lower error measurement and perform better.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| 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".