Comparative Analysis of Gold Forecasting Models: Statistical vs. Machine Learning Approaches

Gold is an important asset class, serving as both a store of value and a hedge against inflation and market uncertainty. Therefore, performing predictive analysis of gold prices is essential. Reference [1] evaluated several predictive methods for gold prices. It examined not only classical, statistical approaches but also newer machine learning techniques. The study used data from 2021 to 2025, with 80% as in-sample data and 20% as validation data.

The authors pointed out,

The results highlight several key findings. First, descriptive and diagnostic analysis confirmed that gold remains a moderately volatile but consistently appreciating asset, with historical returns of 85% over the period. Among the forecasting models, Linear Regression and ETS outperformed ARIMA, KNN, and SVM, achieving the lowest error rates (RMSE 35.7) and the highest explanatory power (R² 0.986). Contrary to common assumptions, machine learning models such as KNN and SVM failed to surpass traditional statistical approaches, underlining the importance of model interpretability and stability in volatile markets. Forecasts for 2026 indicate a projected average price of $4,659, representing a potential 58.6% return, though results also highlight the necessity of caution given market uncertainties. Collectively, the findings demonstrate that simpler models can often provide more reliable forecasts than complex algorithms when applied to financial time series.

In short, the paper shows that,

• Linear Regression and ETS outperformed ARIMA, KNN, and SVM, delivering the lowest error and highest explanatory power,
• Machine learning models (KNN, SVM) did not outperform traditional statistical methods, emphasizing the value of interpretability and stability in volatile markets.

Overall, simpler models often provide more reliable forecasts than complex algorithms for gold time series.

Another notable aspect of the study is its autocorrelation analysis, which reveals that, unlike equities, gold does not exhibit clear autocorrelation patterns—its price behavior appears almost random. The paper also suggested improving the forecasting model by incorporating macroeconomic variables.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Muhammad Ahmad, Shehzad Khan, Rana Waseem Ahmad, Ahmed Abdul Rehman, Roidar khan, Comparative analysis of statistical and machine learning models for gold price prediction, Journal of Media Horizons, Volume 6, Issue 4, 2025

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