A Study On Gold Price Prediction Using Machine Learning

DOI : 10.17577/NCRTCA-PID-069

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A Study On Gold Price Prediction Using Machine Learning

Ram Prasad S K

PG Student, Department of MCA Dayananda Sagar College of Engineering, Bangalore, Karnataka, India

Dr. Vibha M B

Assistant Professor, Department of MCA, Dayananda Sagar College Of Engineering, Bangalore, Karnataka,India

AbstractMachine 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.

KeywordsMachine learning, Gold prices, Historical data, Algorithms.


    Gold, being a precious metal with perceived value and stability, has become a popular investment asset [1]. The price of gold is influenced by various factors such as the global economy, inflation, currency fluctuations, and geopolitical events [2]. Accurate forecasting of gold prices is crucial for informed decision- making by traders and investors.

    In recent years, machine learning algorithms have gained traction in the field of gold price prediction [3]. These algorithms analyze large historical datasets, uncovering patterns and trends that might go unnoticed by humans [4]. By considering factors like supply and demand, geopolitical events, and economic indicators, these algorithms can provide reasonably accurate predictions of future gold prices [5]. This paper's

    overview emphasizes the importance of precise prediction and the advantages of using machine learning techniques for gold price forecasting.

    The paper outlines the key steps involved in constructing a machine learning model for gold price prediction and explores popular algorithms employed in this domain [6]. It also discusses the challenges associated with machine learning- based gold price prediction and proposes potential avenues for future research [15]. Throughout history, gold has been valued as a store of wealth and a form of currency, and its price is influenced by various variables such as economic data, geopolitical developments, supply and demand, and investor sentiment [28]. Predicting the future value of gold can be challenging due to the market's complexity and volatility.

    Machine learning algorithms serve as powerful tools for analyzing and forecasting the prices of financial assets, including gold [13]. These machine learning algorithms utilize substantial historical data to uncover trends, patterns, and correlations between multiple factors influencing gold prices. Gold price prediction using machine learning involves analyzing historical data, identifying patterns, and utilizing them to make predictions about future gold prices [7]. Machine learning models can consider a range of factors such as global economic indicators, geopolitical events, and market trends, as they are trained to predict gold prices accurately.

    To make gold price predictions using machine learning, the initial step involves gathering historical data on gold prices and relevant economic and market indicators [33]. This data is then processed and cleaned to ensure its suitability for use with machine learning algorithms [34]. Subsequently, a machine learning model is trained using the preprocessed data. The model's parameters are adjusted to optimize its performance [35]. Once the model is trained, it can be deployed to make predictions regarding future gold prices.

    The objective of this study is to develop a forecasting model capable of accurately predicting gold prices over an extended time frame [14]. The algorithm aims to forecast the direction of gold prices and identify the key factors that exert the

    greatest influence on gold pricing [21]. Moreover, the model should distinguish between short-term and long-term trends, enhancing the accuracy of its forecasts [16].

    Gold exchange-traded funds (ETFs) are listed and traded as securities on stock exchanges, such as the National Stock Exchange of India (NSE) and Bombay Stock Exchange Ltd. (BSE) [27]. Similar to corporate stocks, gold ETFs can be bought and sold at market prices in the cash segment of these exchanges.

    Overall, the utilization of machine learning algorithms in gold price prediction provides valuable insights for traders and investors, aiding them in making informed decisions [12].


    The utilization of machine learning for gold price prediction presents a significant challenge. This challenge entails developing a model that can accurately forecast future gold prices based on a historical dataset comprising gold prices and pertinent variables like economic indicators, geopolitical events, and supply and demand [25].

    This challenge presents several complexities. Firstly, the gold market is intricate and unpredictable, influenced by numerous elusive variables [31]. Secondly, traditional statistical models may struggle to adequately capture the nonlinear and dynamic relationships between these variables and gold prices [10]. Lastly, the accuracy of predictions can be influenced by factors such as the quality and quantity of training data, as well as the selection of algorithms and hyperparameters [26].

    To address these challenges, researchers have developed various machine learning techniques and strategies, including time series analysis, regression analysis, deep learning, and ensemble methods [30]. These approache aim to accurately estimate future prices by capturing the intricate correlations among the different factors impacting gold prices [13]. The choice of technique depends on the specific problem and data, as there is no one-size-fits-all solution applicable to all scenarios [33].


    Researchers conducted a study on gold price estimation, utilizing the forecasting tool Facebook Prophet in conjunction with a historical dataset spanning from 1968 to 2019 [31]. Additional inputs included economic and financial statistics like inflation rates, exchange rates, and stock market indexes. The findings indicated that the Facebook Prophet model accurately predicted gold prices for the following year, with an average mean absolute error (MAE) of 10.4 USD.

    Another study focused on estimating gold prices using Facebook Prophet and a historical price dataset covering the period from 1970 to 2018 [26]. Technical indicators such as moving averages, relative strength index (RSI), and stochastic oscillators were incorporated as supplementary inputs. The results demonstrated the Facebook Prophet model's accurate forecasting of gold prices for the subsequent month, with an average mean absolute error (MAE) of 3.5 USD.

    A third study employed Facebook Prophet to estimate future gold prices by analyzing historical gold prices from 1995 to 2019 [27]. In addition to historical prices, macroeconomic and financial data, including interest rates, currency rates, and stock market indices, were considered as inputs to the model.

    Moreover, a comparative study evaluated the forecasting performance of Facebook Prophet against established time series algorithms like ARIMA and exponential smoothing state space model (ETS) [8]. The study utilized a historical price dataset spanning from 1979 to 2020 and concluded that Facebook Prophet exhibited higher accuracy, with an average mean absolute error (MAE) of 9.86 USD compared to 11.85 USD and

    11.58 USD for ARIMA and ETS, respectively.

    The results indicate that Facebook Prophet can reliably predict gold prices for both short- and long-term timeframes, with average MAEs ranging from 3.5 to 10.4 USD. Furthermore, Facebook Prophet has shown superior accuracy compared to well-established time series forecasting algorithms such as ARIMA and ETS.


    Fig. 1. Gold price prediction workflow

    The gold price prediction process using Facebook Prophet can be divided into four main steps, each utilizing different Python modules and libraries. These steps are:

    1. Data Collection:

      The initial step involves gathering historical gold price data from reliable sources such as financial databases like Yahoo Finance, Quandl, or the World Gold Council. The collected data should include the date and corresponding gold price.

    2. Data Preprocessing:

      After gathering the data, it is necessary to perform data cleaning and preparation in order to make it suitable for analysis. This involves handling missing values, addressing outliers, and formatting the data into a time series structure with the date as the index and the gold price as the value.

    3. Facebook Prophet Model:

      The Facebook Prophet model has been widely acknowledged as a powerful tool for gold price prediction [25][32]. Its ability to handle both seasonal and non-seasonal data, along with the incorporation of external variables, has been shown to enhance the accuracy of predictions [25][32]. The model's utilization of Bayesian inference ensures robustness and provides interpretable results [25]. Comparative studies have demonstrated that the Facebook Prophet model outperforms traditional techniques like ARIMA in gold price prediction [25][32]. However, the challenges associated with gold price prediction, such as market complexity and external factors, should be taken into account, and cautious decision-making is recommended based on the model's forecasts [32].

      Fig. 2. Facebook Prophet model

    4. Model Fitting:

      After data preprocessing and configuring the method, the model is fitted to the historical data. The Prophet algorithm employs a Bayesian framework to fit a generalized additive model to the time series data.

    5. Model Evaluation:

      The effectiveness of the trained Facebook Prophet model is assessed using test data. Evaluation metrics such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) are used to measure the model's performance. The accuracy of the Facebook Prophet model is compared to other established time series forecasting algorithms using these metrics.

    6. Forecasting:

    The final step involves generating forecasts for future gold prices using the trained model. The Prophet algorithm predicts gold prices for a specific time horizon, such as the next day, week, or month. The forecasted values can be visualized by plotting them against the actual values to evaluate the accuracy of the predictions.

    In addition to the Facebook Prophet algorithm, several Python modules are utilized in implementing the gold price prediction model:

    1. Pandas: A Python package used for data analysis and manipulation. It is employed to preprocess the time series data and prepare it for input into the Prophet algorithm.

    2. Matplotlib: This Python package enables the creation of visualizations such as graphs, charts, and plots. It is used to display historical gold prices and the price predictions generated by the Prophet algorithm.

    3. NumPy: A Python module that provides support for large, multi-dimensional arrays and matrices. The gold price forecasting model utilizes NumPy for calculations and numerical operations.

    4. Scikit-learn: A Python machine learning toolkit offering algorithms for clustering, classification, and regression tasks. It can be utilized to assess the effectiveness of the Prophet model.


    In our comprehensive study on gold price prediction using machine learning, we compared several models, including Prophet, ARIMA, ETS (Exponential Smoothing), and other machine learning algorithms. The goal was to assess their performance and determine the most effective approach for forecasting gold prices.

    Prophet, developed by Facebook, has gained popularity for its ability to handle time series data and capture seasonality and trend components. Previous studies have shown the efficacy of Prophet in gold price prediction[25][32]. It is particularly advantageous for its simplicity of implementation and minimal hyperparameter tuning requirements [25]. However, Prophet may have limitations in capturing complex patterns and handling external factors that influence gold prices [32].

    ARIMA (Autoregressive Integrated Moving Average) is a traditional time series forecasting model that has been widely used in various domains, including gold price prediction. ARIMA models capture the linear dependencies in the data and are capable of handling trend and seasonality. However, ARIMA may struggle with non-linear patterns and may not fully capture the complexities of gold price dynamics, especially when external factors come into play [17][18].

    ETS (Exponential Smoothing) is another traditional time series forecasting method that has been applied to gold price prediction. ETS models

    are suitable for capturing trend and seasonality, but they may face challenges when dealing with non-linear patterns and external influences[19].

    Comparing these models, Prophet has shown promising results in capturing both seasonality and non-seasonality in gold price data. It outperforms ARIMA and ETS in terms of accuracy and computational efficiency[32][25]. However, its limitations lie in the complexity of external factors that can significantly impact gold prices, which may not be adequately captured by the model [25].

    To improve gold price prediction, several aspects can be addressed. Firstly, incorporating external variables, such as economic indicators, geopolitical events, and supply-demand dynamics, could enhance the models' ability to capture the impact of these factors on gold prices [20][21]. Including such variables in Prophet, ARIMA, or ETS models as exogenous inputs may help improve their predictive accuracy [25].

    Moreover, ensemble methods, such as combining the predictions of multiple models, can potentially lead to improved accuracy and robustness. Ensemble approaches, such as model averaging or stacking, can exploit the strengths of different models and provide more reliable forecasts [22][9].

    In summary, while Prophet has shown promise in capturing seasonality and trends in gold price data, its ability to handle external factors influencing gold prices may be limited. ARIMA and ETS models have their strengths but may struggle with non-linear patterns and external influences. Enhancements could include incorporating external variables and exploring ensemble methods to improve gold price prediction accuracy and robustness.


In recent studies, researchers have demonstrated the efficacy of the Facebook Prophet algorithm specifically for predicting gold prices. This algorithm has been designed to handle time series data and has shown remarkable versatility and robustness by accommodating both seasonal and non-seasonal patterns in the data.

One of the notable advantages of the Facebook Prophet algorithm is its ability to incorporate external variables, such as economic indicators, into the forecasting process. By considering these additional factors, the algorithm aims to enhance the accuracy of its predictions.

Comparative research has been conducted to evaluate the performance of the Facebook Prophet algorithm against traditional time series forecasting techniques like ARIMA and exponential smoothing. The findings indicate that the Facebook Prophet algorithm outperforms these traditional methods in terms of both accuracy and computational efficiency. Not only does it deliver more accurate forecasts, but it also requires minimal hyperparameter tuning and is relatively simple to implement. This ease of use and its promising performance make the Facebook Prophet algorithm an attractive choice for real- world applications in gold price prediction.

However, it is essential to acknowledge the limitations associated with using the Facebook Prophet algorithm for gold price forecasting. External factors, such as political developments and global economic conditions, can significantly influence gold prices. These factors introduce uncertainties and challenges in accurately predicting future price movements. While the Facebook Prophet algorithm incorporates external variables, it is important to recognize that the precise impact of these factors on gold prices can be complex and difficult to capture completely.

In summary, the Facebook Prophet algorithm shows promise in predicting gold prices and providing valuable insights for traders, investors, and decision-makers. Its ability to handle both seasonal and non-seasonal patterns, integrate external variables, and outperform traditional forecasting techniques makes it an appealing choice. However, it is crucial to exercise caution and consider the limitations and uncertainties associated with the forecasts. Understanding the potential influence of external factors on gold prices is essential when making investment decisions based on the predictions generated by the Facebook Prophet algorithm.


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