Predicting Future Gold Rates using Machine Learning Approach

Sami, Iftikhar ul; Junejo, Khurum Nazir

doi:10.14569/ijacsa.2017.081213

Cited by 18 publications

(5 citation statements)

References 3 publications

(5 reference statements)

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“…The LSTM utilizes a progression of 'gates,' each with its own RNN, to keep, neglect or disregard information focuses given a probabilistic model. Problems with explosive and disappearing gradients can also be solved with LSTMs [24] [25].…”

Section: Univariate Lstmmentioning

confidence: 99%

Gold prices prediction: Comparative study of multiple forecasting models

Trivedi¹,

Somvanshi²,

J³

2022

YMER

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Gold is a rare and valuable metal as its price has attention globally. Gold price varies very frequently, almost daily and it is in high focus by the government, investors, and industrialists. Trends of Increasing in the Gold price indicate gold is also one of the better investment plans to yield better profits in future. Forecasting the gold price is a primary financial problem for a lot of people as it is considered an investment asset and gold is used as raw material in industrial products.Hence forecasting the gold price is much needed for the financial economy of any nation. Implementing an accurate forecasting technique to predict the gold price is Crucial. Better investments in gold can be made only when we predict the price meticulously. This research paper focuses on forecasting the gold price by implementing multiple forecasting models to identify which model performs better on the dataset to get better error measures. Keywords: Time Series Prediction, ARMA, ARIMA, LSTM, MAPE, RMSE

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Section: Univariate Lstmmentioning

confidence: 99%

Gold prices prediction: Comparative study of multiple forecasting models

Trivedi¹,

Somvanshi²,

J³

2022

YMER

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“…XGBoost, a widely used model in demand forecasting due to its strong performance in sales forecasting for retail, was found to be a favorable choice [19]. The performance of XGBoost surpassed other models in predicting gold rates [20]. Moreover, XGBoost outperformed artificial neural networks and Support Vector Regression in groundwater level prediction [21].…”

Section: Introductionmentioning

confidence: 99%

A Sales Forecasting Model for New-Released and Short-Term Product: A Case Study of Mobile Phones

Hwang,

Yoon,

Baek

et al. 2023

Electronics

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In today’s competitive market, sales forecasting of newly released and short-term products is an important challenge because there is not enough sales data. To address these challenges, we propose a sales forecasting model for new-released and short-term products and study the case of mobile phones. The main approach is to develop an integrated sales forecasting model by training the sales patterns and product characteristics of the same product category. In particular, we analyze the performance of the latest 12 machine learning models and propose the best performance model. Machine learning models have been used to compare performance through the development of Ridge, Lasso, Support Vector Machine (SVM), Random Forest, Gradient Boosting Machine (GBM), AdaBoost, LightGBM, XGBoost, CatBoost, Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). We apply a dataset consisting of monthly sales data of 38 mobile phones obtained in the Korean market. As a result, the Random Forest model was selected as an excellent model that outperforms other models in terms of prediction accuracy. Our model achieves remarkable results with a mean absolute percentage error (MAPE) of 42.6258, a root mean square error (RMSE) of 8443.3328, and a correlation coefficient of 0.8629.

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“…Gold price fluctuations are price changes that occur in a certain period of JURNAL VARIAN | e-ISSN: 2581-2017 time, which can be influenced by various factors. One of the influencing factors is the monetary policy of the central bank, such as interest rates and inflation (ul Sami and Junero, 2017). When interest rates and inflation are low, the value of the currency becomes stronger and gold prices tend to decrease, and vice versa.…”

Section: A Introductionmentioning

confidence: 99%

“…These studies developed a model to forecast the global gold price throughout the COVID-19 pandemic, solely relying on historical gold price data and excluding any external factors that could impact the model. Meanwhile, there are articles that discuss machine learning algorithms, approaches, and techniques (Pragna et al, 2022;Radhamani et al, 2022;ul Sami and Junero, 2017;Tripurana et al, 2022) that applied machine learning technique to forecast financial financial indicators, with a primary focus on forecasting the price of gold. In another research, discuss the forecasting of gold prices using Generalized Autoregressive Conditional Heteroscedasticity (Garch) model (Haris, 2020); local polynomial nonparametric method equipped with GUI R (Hendrian et al, 2021); average-based fuzzy time series method (Hariwijaya et al, 2020); multiple linear regression method (Sravani et al, 2021); Nearest Neighbor Retrieval method (Nugroho, 2018); data mining techniques (Mahena et al, 2015).…”

Section: A Introductionmentioning

confidence: 99%

Gold Price Fluctuation Forecasting Based on Newton and Lagrange Polynomial Interpolation

Maharani,

Azka,

Darlena

2023

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Gold is a highly valuable commodity and an investment opportunity for many people. However, thereare often significant fluctuations in gold prices that affect investment decisions. Various mathematicalforecasting methods have been developed to anticipate gold price fluctuations. This study uses historicaldaily data of gold prices during January-May 2023. The method used in this study is the Newton andLagrange polynomial interpolation method with several orders to analyze data and forecast gold pricefluctuations. The purpose of this study is to compare the performance and accuracy of the order levels ofthe Newton and Lagrange polynomial interpolation forecasting models. In this study, the test data pointsand orders are selected so that a range is formed that matches the amount of data available. The testorders used in this study include orders 2, 3, 5, 6, and 10. This study found that the 2nd order polynomialinterpolation method is more effective and accurate in forecasting gold price fluctuations compared tothe higher orders tested. This is indicated by the results of the calculation of MAE, RMSE, and MAPEvalues in 2nd order polynomial interpolation which are smaller than in 3rd, 5th, 6th, and 10th orderpolynomial interpolation. This suggests that a polynomial of 2nd order has been able to model andforecast gold price fluctuations well. However, it is important to remember that these conclusions arebased on the data and methods used in this study. Variability in forecasting results can occur dependingon the quality of the data, the time period used, and the interpolation method applied, among others.Therefore, further research and wider testing needs to be conducted to validate these conclusions.

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Predicting Future Gold Rates using Machine Learning Approach

Cited by 18 publications

References 3 publications

Gold prices prediction: Comparative study of multiple forecasting models

Gold prices prediction: Comparative study of multiple forecasting models

A Sales Forecasting Model for New-Released and Short-Term Product: A Case Study of Mobile Phones

Gold Price Fluctuation Forecasting Based on Newton and Lagrange Polynomial Interpolation

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