Gold Price Prediction Research Articles

Underlying factors for gold price prediction based on ARIMA models

Abstract. The purpose of this study is to explore the application of ARIMA model and deep learning technology in time series diagram prediction and the comparison of their effects. First, this paper analyzes the time series data in detail and models them using the ARIMA model. The ARIMA model effectively captures the seasonal and trending characteristics of the data through autoregressive, moving average, and differential steps. At the same time, the method of introducing deep learning models based on recurrent neural networks (RNNs), especially long short-term memory networks (LSTMs), to deal with the nonlinear characteristics and long-term dependency of time series data. By comparing and analyzing the performance of the ARIMA model and the deep learning model in terms of prediction accuracy, computational efficiency and model generalization ability, finding that the deep learning model has higher prediction accuracy when processing complex time series data. In addition, this study also uses time series diagrams to visually display the prediction results, which further verifies the advantages of deep learning models in capturing dynamic changes in data. This study provides a new perspective and method for time series data analysis and prediction, and has important practical significance for finance, meteorology, energy and other fields.

Predicting Gold Prices Through Machine Learning with Economic Indicators and Feature Selection Methods

The enormous fluctuation of the gold price in international markets gives predictive analysis an indispensable place for business firms, economists, and investors. With this in mind, this research aims to construct a machine learning model for gold price prediction based on past information and identify some leading patterns affecting the prices of such a commodity. The model thus covers a range of related financial and economic factors, be it past gold prices and inflation metrics, oil price vagaries, worldwide market indexes, and interest rate movements. Using machine learning algorithm approaches like Support Vector Machine (SVM), Random Forest, as well as Linear Regression approaches, the system is set to identify complex relationships prevailing in the data and provides predictions for short-term movements and long-term movements concerning price. The model will be trained and validated using historical data that includes the history of the gold price. In this respect, the findings of the study are relevant to stakeholders in financial forecasting who could make informed investment decisions. Additionally, the paper has outlined the method for feature selection that determines which factors are dominant in explaining the fluctuation of the gold price. The goal of the model, therefore, is to enhance its performance through prediction accuracy reduction in noise, recursive elimination of features, and application of correlation analysis. As a result, a robust evaluation of several machine algorithms, including support vector machines, allows the selection of a suitable model that best solves the prediction task. This approach to forecasting will therefore aid in the creation of instruments for risk management, automated trading techniques, and financial strategies for an individual as well as for organizations. Keywords: Support Vector Machine, Random Forest, machine learning, and gold price prediction; economic indicators, feature selection, and linear regression in the field of financial forecasting; erratic behaviour; and risk management.

Research on Gold Price Prediction Based on LSTM Modeling

Gold plays a pivotal role in asset allocation, and the construction of gold price prediction models represents a complex yet rewarding task within the field of finance. The problem of international gold price is addressed in this paper forecasting by proposing a standard Long-Short Term Memory (LSTM) model and introducing bi-directional LSTM (Bi-LSTM) networks and multivariate analysis to compare the forecasting accuracy of the relevant models. This comparison is based on the daily gold price of the London Bullion Market Association (LBMA) for 2013-2022. This methodology takes into account various factors that influence the gold market and incorporates them as input sources into the Multi-factor LSTM model, thereby enhancing the interpretability of the LSTM model. Correlation analysis and the Granger test are employed to analyze these influencing factors. However, the results of this study suggest that while the Multi-factor LSTM model may be more accurate in predicting outliers, it tends to increase the number of small errors. On the other hand, the inclusion of Bi-LSTM networks can improve the overall accuracy of gold price prediction.

Artificial intelligence techniques recruitment for gold ore (Crude gold) price prediction

Artificial intelligence techniques recruitment for gold ore (Crude gold) price prediction

Gold Price Prediction Using Two-layer Decomposition and XGboost Optimized by the Whale Optimization Algorithm

Gold Price Prediction Using Two-layer Decomposition and XGboost Optimized by the Whale Optimization Algorithm

A Research of Fine-Tuning CNN-LSTM Model for Gold Price Prediction

Gold price and foreign exchange price prediction are usually regarded as serialized data with strong time correlation. It is precisely because of this feature that the performance of traditional convolutional neural networks is often not as good as that of recurrent neural networks. This paper fine-tunes the CNN-LSTM model in two ways. The first is to simplify the input data, convert the time series into binary data, and change the model task from predicting prices to predicting price fluctuations. The second is to add a dropout layer and use the dropout layer to improve the model's overfitting problem. The results demonstrate that the improved model outperforms the first model, with prediction accuracy rising from 54% to 57%. Furthermore, the refined model is not limited to processing the gold price series; it can also be applied to other time series, and when paired with other models, it could yield even better outcomes.

Research on gold price prediction based on ARIMA model

Gold is one of the most prevalent currencies in the world and its price has a very strong influence in the global financial markets. Gold has safe-haven properties, which can have a significant impact on its demand and price, especially in times of social unrest or financial crisis. Now, the demand for gold by investors has increased dramatically. Therefore, being able to accurately predict the direction of the gold price can help investors to effectively develop investment strategies and risk management measures. The overall objective of this study is to forecast the price of gold futures for the next six months. In this study, the Kaggle website was searched to find the price of gold from 2020 to 2024 and finally the CLOSE price was chosen as the final predicted price. This paper uses the ARIMA model for gold price forecasting. By comparing the RSME size of each model, ARIMA (1, 1, 2) is finally chosen. From the prediction results the price of gold remains stable in the first half of the year and then increases significantly. From the results of the residual test, there is no autocorrelation, and then it is white noise.

Machine Learning Models for Gold Price Prediction: A Comparative Analysis and Evaluation

The prediction of gold prices is meaningful for multiple industries. Gold is a relatively stable store of value, and its price is closely linked to stocks, exchange rates, and monetary policy. Observing the trend of gold prices can promote the stable development of financial businesses. In this article, the daily opening and closing prices of gold over the past ten years were chosen as the data support. Seven different models were trained to predict prices on a yearly basis with a half-year interval. These predictions were then compared to actual data, and evaluations and analyses were conducted to identify the most suitable model for gold price forecasting. The machine learning models used included Ridge Regression, Linear Regression, Decision Tree Random Forest, Lasso Regression, Support Vector Machine, and K-Nearest Neighbors. In the evaluation process, Mean Squared Error (MSE) was used as the criterion to assess the accuracy of the model predictions.

The Comparison Between Random Forest and LSTM Models Based on the Gold Price Prediction

The gold price forecast is crucial for financial institutions, investors, mining firms, and associated enterprises. Precise price prediction is especially important in the gold market because of its volatility and unpredictability; this has important consequences for strategy formulation and decision making. In order to give practitioners a better prediction technique to increase the accuracy of market trading, the aim of this study is to assess the predictive effectiveness of the two models in predicting the gold market. The study forecasts gold prices using the Random Forest and Long Short-Term Memory (LSTM) models, comparing their performances using five assessment metrics. The end findings prove that the Random Forest method outperforms the LSTM model in comparison. In particular, the Random Forest model predicts more accurately and with less inaccuracy. The study finds that, while the Random Forest model is more accurate, both the LSTM model and the Random Forest model are appropriate techniques for gold market projections. To increase the precision and consistency of the predictions, future studies will keep refining the LSTM model and look for causes for its subpar performance.

Comparative Performance of Neural Network Architectures in Gold Price Prediction: a Study of GRU, N-BEATS, LSTM and Hybrid Models

This study assesses the efficacy of a variety of neural network models in predicting gold prices, with a particular emphasis on GRU, N-BEATS, N-BEATS-GRU, N-BEATS-LSTM, and LSTM. We evaluated the models using a comprehensive dataset and metrics such as MSE, MAE, MAPE, and RMSE. The GRU model demonstrated the highest MAPE of 3.10%, as indicated by the results. N-BEATS was the second-best-performing model. In stark contrast to expectations, hybrid models, notably N-BEATS-LSTM, underperformed. Compared to LSTM-based models, visual analysis demonstrated that GRU and N-BEATS more effectively captured short-term and long-term trends. The results indicate that simplified models, such as GRU, may be more effective than complex hybrids in predicting gold prices.

The Implementation of Gated Recurrent Unit (GRU) for Gold Price Prediction Using Yahoo Finance Data: A Case Study and Analysis

The Implementation of Gated Recurrent Unit (GRU) for Gold Price Prediction Using Yahoo Finance Data: A Case Study and Analysis

Gold Price Prediction Using Skewness and Kurtosis Based Generalized Auto-regressive Conditional Heteroskedasticity Approach with Long Short Term Memory Network

Gold Price Prediction Using Skewness and Kurtosis Based Generalized Auto-regressive Conditional Heteroskedasticity Approach with Long Short Term Memory Network

An Enhanced Study on Gold Price Prognosis using Machine Learning

Machine learning has emerged as a prominent research area for predicting gold prices, utilizing historical data and algorithms. The field aims to uncover patterns, trends, and connections among various factors that influence gold prices, including economic indicators, geopolitical events, and supply and demand dynamics. By employing machine learning algorithms, predictive models can be constructed to provide valuable insights into potential patterns in gold price movements. This enables traders, investors, and other stakeholders to make informed decisions when it comes to gold investments. In our study, we delve into the realm of data science and machine learning techniques to forecast gold prices. We meticulously analyze historical gold price data, develop sophisticated forecasting models, and rigorously evaluate their performance. Through this process, we are able to identify meaningful patterns and correlations that significantly contribute to the prediction of future gold prices. One of the key aspects of our study is the assessment of the reliability and accuracy of various machine learning models specifically designed for gold price prediction. We examine different algorithms and approaches, comparing their effectiveness in capturing the underlying patterns in gold price movements. This evaluation provides us with important findings and insights, enabling us to determine the most suitable models for accurate gold price forecasting. However, it is crucial to acknowledge the limitations inherent in our study. The forecasting of gold prices is a complex task influenced by a multitude of factors, some of which may be unpredictable or subject to sudden changes. Therefore, our models may not capture all the nuances and intricacies of gold price dynamics. To address these limitations, we propose recommendations for future research, such as exploring novel data sources, incorporating additional variables, or improving the models' adaptability to changing market conditions. Machine learning plays a pivotal role in the field of gold price prediction. By leveraging historical data and employing sophisticated algorithms, we can uncover valuable insights and patterns that assist in forecasting future gold prices. Our study aims to contribute to this growing body of research by developing reliable models and providing important insights for traders, investors, and other stakeholders in the gold market

Gold Price Prediction Model Based on LSTM Neural Network and ARIMA

Gold Price Prediction Model Based on LSTM Neural Network and ARIMA

Artificial Intelligence-Driven Model for Gold Price Prediction

This study introduces an innovative approach to forecasting gold prices by employing Artificial Intelligence (AI)--driven models. Utilizing advanced machine learning techniques, including Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine (SVM), the research evaluates the predictive capabilities of these models through comprehensive assessments based on key performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2 Error. A particular focus is placed on ensemble learning, exemplified by the Random Forest model, which demonstrates superior accuracy in capturing intricate patterns within gold price data. These findings contribute valuable insights to the field of financial forecasting, emphasizing the potential of AI-driven models to inform stakeholders in gold investment and financial markets. The study concludes by advocating for ongoing research and continuous model refinement to adapt to dynamic market conditions and enhance the precision of gold price predictions. Keywords: gold price prediction artificial intelligence, MSE.

Data Mining Strategies for Gold Price Prediction Using Multi-Factorial Influences

The prediction of gold prices is a critical endeavor in the realm of financial markets, given the precious metal's significance as a store of value and its susceptibility to various economic, geopolitical, and market influences. This research focuses on the application of machine learning (ML) models to predict gold rates by incorporating factors such as the dollar exchange rate, crude oil prices, and element prices. The objective is to utilize advanced ML algorithms, including regression, decision trees, and ensemble methods, to analyze and interpret the complex relationships between these key factors and gold prices. Through the use of robust ML models and historical data, the study aims to improve the accuracy of gold rate predictions. Additionally, the research seeks to explore the importance of features and interactions among variables to develop comprehensive predictive models. The utilization of ML techniques in this context is expected to provide a deeper understanding of the dynamic influences of the dollar exchange rate, crude oil prices, and element prices on gold rates, offering valuable insights for investment and risk management strategies in financial markets.

Prediction of Gold price with comparison of forecasting methods

Gold has emerged as an extra famous and very beneficial commodity in phrases of investment. Gold has been considered, as a country wide reserved commodity for many years, which leads to very integral for the economy of any country. Most people and traders believe that gold is a protected investment from uncertainty and political chaos. The rate of motion of gold helps the buyers from the centre of attention in their investments; they make use of the year by year information from Indian Gold Council. The analysis of the data was taken from 1964 to 2020. This paper's motto is to analyze and summarize different algorithms for predicting the rate of gold. The procedures utilized to fit the data were from the Time Series analysis Auto Regressive Integrated Moving Average (ARIMA) and Neural Network models; Multi-Layer Perception (MLP) and Extreme Learning Machine (ELM). The test data were utilized for the analysis, and then the outcome was exhibited with the help of error parameters. ELM is best as compared to ARIMA and MLP. The error measures are RMSE (1634.975) and MAPE (3.002). The error measurements have been represented in the tables for ARIMA and MLP. The best prediction of Gold price was given by the ELM, which is be efficient and accurate model.

Application of the Support Vector Regression Method with the Grid Search Algorithm to Predict Movement Gold Price

Gold is an investment with the smallest risk because it can be sold anytime and anywhere. In Indonesia, gold bullion as an investment product is known for its purity level of 99.99%, namely gold bullion produced by PT. Aneka Tambang (Antam) through its Precious Metals business unit. Apart from its pure production, Antam gold bullion is easier to resell anytime and anywhere because it has an official certificate from the international gold standardisation institution, namely LBMA (London Bullion Market Association), to more easily estimate the value of gold bullion when sold. To overcome this, predictions of future gold prices are needed. In this research, one of the prediction methods is Support Vector Regression with the Grid Search Algorithm. In this method this method will be used to predict the price of gold, which aims to predict and find out the price of gold one year in the future to produce a level accuracy (MAPE) of 5.43% and the prediction of gold prices increasing from 2023-June-01 to 2024-March-23 while experiencing a decline starting in 2024-March-24. Research by examining the relationship between variables, which emphasises data consisting of numbers so that it is analysed based on statistical procedures using the Support Vector Regression method with data sourced from the daily price of gold bullion through PT. Gallery 24 Pawnshops, North Sumatra. Where this method is very well used in predicting by choosing the best kernel used is the linear kernel because, from these three kernels, the best hyperparameters were obtained for predicting gold price movements using a linear kernel with a division for training and testing data of 60: 40. The MAPE value obtained was 5.43.

Exploring the effectiveness of a multilayer neural network model for gold price prediction

Exploring the effectiveness of a multilayer neural network model for gold price prediction

Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning.

Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs' parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN's outputs to predict the daily closing gold price.