Applying Machine Learning Techniques to Analyze and Explore Precious Metals
Bibliographic record
Abstract
This paper examines the utilization of machine learning methods to predict the values of valuable metals, specifically gold, silver, palladium, and platinum, from 2017 to 2023. Accurate price prediction for these commodities is tough yet crucial for investors and stakeholders due to their volatile nature, influenced by macroeconomic, geopolitical, and market-specific factors. We utilize historical price data to create and assess various machine-learning models to improve predicting accuracy. It utilizes machine learning techniques, specifically the Gaussian Mixture Model (GMM), to accurately collect and analyze the patterns present in the data. The study entails thorough data preprocessing, which encompasses cleaning and normalization, and models undergo training and validation through cross-validation techniques. Their performance is assessed using metrics such as Entropy, Log-Likelihood, Normalized Entropy Criterion (NEC), Integrated Completed Likelihood (ICL), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Our research shows that machine learning models offer higher forecasting capabilities. In addition, the prices of precious metals saw a substantial rise during the COVID-19 pandemic due to increased demand for secure investments, industrial usage, favorable monetary policies, worries about inflation, and a devalued US dollar. The pandemic underscored the dual nature of precious metals as both valuable metals and commodities used in industries, leading to an increase in their price during this time. The paper provides advice for investors and policymakers on how to utilize machine learning-driven insights to make well-informed decisions in the precious metals market.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".