Multi-objective optimization of gold price forecasting using the pareto alpha-cut technique
Accurate forecasting of gold prices is crucial for financial decision-making in various sectors, including investment and mining. This study introduces a multi-objective optimization framework that utilizes the Pareto alpha-cut technique to evaluate and enhance forecasting models for gold prices. We...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125003784 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849389656817270784 |
|---|---|
| author | Pullooru Bhavana |
| author_facet | Pullooru Bhavana |
| author_sort | Pullooru Bhavana |
| collection | DOAJ |
| description | Accurate forecasting of gold prices is crucial for financial decision-making in various sectors, including investment and mining. This study introduces a multi-objective optimization framework that utilizes the Pareto alpha-cut technique to evaluate and enhance forecasting models for gold prices. We employed three distinct models: the Autoregressive Distributed Lag (ARDL) model, a stochastic model, and the Autoregressive Integrated Moving Average (ARIMA) model, to capture the underlying dynamics of gold price fluctuations influenced by macroeconomic factors.The methodology incorporates the Pareto optimality approach combined with fuzzy logic to manage trade-offs among multiple performance metrics, specifically Root Mean Squared Error (RMSE), volatility, and R-squared. By applying the alpha-cut technique, we filtered out less optimal models, retaining only those that met a predefined level of acceptability across all criteria.Results indicate that the ARDL model consistently outperformed the others, achieving superior accuracy and fit, while the stochastic model exhibited robust stability. This framework not only facilitates the identification of Pareto optimal models but also provides valuable insights into the balance between accuracy and stability in gold price forecasting. The findings contribute to a deeper understanding of forecasting methodologies and highlight the practical implications for stakeholders in the financial and commodity sectors. • This study introduces a multi-objective optimization framework leveraging the Pareto alpha-cut technique. • Compared with ARDL, ARIMA and Stochastic mode • The validity analysis confirms the accuracy and stability of gold price forecasting. |
| format | Article |
| id | doaj-art-9637697ff1c642ebb3b754252a56c5ee |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-9637697ff1c642ebb3b754252a56c5ee2025-08-20T03:41:54ZengElsevierMethodsX2215-01612025-12-011510353410.1016/j.mex.2025.103534Multi-objective optimization of gold price forecasting using the pareto alpha-cut techniquePullooru Bhavana0Corresponding author.; Assistant Professor, Sreenivasa Institute of Technology and Management Studies, IndiaAccurate forecasting of gold prices is crucial for financial decision-making in various sectors, including investment and mining. This study introduces a multi-objective optimization framework that utilizes the Pareto alpha-cut technique to evaluate and enhance forecasting models for gold prices. We employed three distinct models: the Autoregressive Distributed Lag (ARDL) model, a stochastic model, and the Autoregressive Integrated Moving Average (ARIMA) model, to capture the underlying dynamics of gold price fluctuations influenced by macroeconomic factors.The methodology incorporates the Pareto optimality approach combined with fuzzy logic to manage trade-offs among multiple performance metrics, specifically Root Mean Squared Error (RMSE), volatility, and R-squared. By applying the alpha-cut technique, we filtered out less optimal models, retaining only those that met a predefined level of acceptability across all criteria.Results indicate that the ARDL model consistently outperformed the others, achieving superior accuracy and fit, while the stochastic model exhibited robust stability. This framework not only facilitates the identification of Pareto optimal models but also provides valuable insights into the balance between accuracy and stability in gold price forecasting. The findings contribute to a deeper understanding of forecasting methodologies and highlight the practical implications for stakeholders in the financial and commodity sectors. • This study introduces a multi-objective optimization framework leveraging the Pareto alpha-cut technique. • Compared with ARDL, ARIMA and Stochastic mode • The validity analysis confirms the accuracy and stability of gold price forecasting.http://www.sciencedirect.com/science/article/pii/S2215016125003784Gold price forecastingMulti-objective optimizationPareto alpha-cut techniqueForecasting modelsPerformance metrics |
| spellingShingle | Pullooru Bhavana Multi-objective optimization of gold price forecasting using the pareto alpha-cut technique MethodsX Gold price forecasting Multi-objective optimization Pareto alpha-cut technique Forecasting models Performance metrics |
| title | Multi-objective optimization of gold price forecasting using the pareto alpha-cut technique |
| title_full | Multi-objective optimization of gold price forecasting using the pareto alpha-cut technique |
| title_fullStr | Multi-objective optimization of gold price forecasting using the pareto alpha-cut technique |
| title_full_unstemmed | Multi-objective optimization of gold price forecasting using the pareto alpha-cut technique |
| title_short | Multi-objective optimization of gold price forecasting using the pareto alpha-cut technique |
| title_sort | multi objective optimization of gold price forecasting using the pareto alpha cut technique |
| topic | Gold price forecasting Multi-objective optimization Pareto alpha-cut technique Forecasting models Performance metrics |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125003784 |
| work_keys_str_mv | AT pulloorubhavana multiobjectiveoptimizationofgoldpriceforecastingusingtheparetoalphacuttechnique |