Bibliographic record
Abstract
Gold futures prices are affected by many factors such as macroeconomics, market supply and demand, and financial markets. Their volatility is crucial to investment decisions and market stability. However, the complexity of the market makes it difficult for traditional forecasting methods to accurately capture the law of price changes. It is of great significance to study efficient forecasting methods. This paper systematically reviews the factors affecting gold futures prices and forecasting methods. The key driving factors are analyzed from the aspects of inflation rate, interest rate, crude oil price, and US dollar index, and three main forecasting methods are summarized: traditional time series models (ARIMA, GARCH, ARDL), hybrid models (MS-MIDAS-CJ, VMD-ICSS-BiGRU) and deep learning methods (SGRU-AM, DBN). Finally, this paper explores the optimization directions of model computational efficiency, generalization ability, and market sentiment integration. This study provides a systematic analysis of gold futures market forecasting. In the future, the combination of deep learning and market sentiment analysis is expected to improve forecasting accuracy and provide support for investors and market decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".