Forecasting Gold Price Trends in Vietnam in the Fourth Quarter of 2025 Using the Arima Model
Bibliographic record
Abstract
In the context of geopolitical instability taking place in the world, leading to the outbreak of global financial crisis. Financial investors tend to invest in gold as a safe "haven" channel with high profitability (DUNG, 2004) . However, gold prices fluctuate in a complex, random, nonlinear manner and are strongly affected by interest rates, inflation, geopolitics, and USD fluctuations (Kristjanpoller & Minutolo, 2015) . This makes forecasting difficult and poses risks for investors and managers. Therefore, scientific and objective tools are needed instead of emotional forecasting. The ARIMA ( Autoregressive Integrated Moving Average) model was developed by two authors (Box & Jenkins, 1976) to forecast time series to analyze the relationship between past and present data to forecast future trends. In this study, ARIMA is applied to forecast gold prices in Vietnam in the fourth quarter of 2025. Based on the data analyzed, the ARIMA(3,2,1) model is the most optimal model to forecast gold prices in Vietnam. The research results are a scientifically based reference source for investors and organizations in making decisions related to gold to minimize risks and maximize profits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".