Prediction of gold price with ARIMA and SVM

Makala, Daniel; Li, Z

doi:10.1088/1742-6596/1767/1/012022

Cited by 35 publications

(25 citation statements)

References 9 publications

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“…LR remained a favorite choice for gold price prediction. Bingol et al [10] have introduced an approach to surveying the relationship of the gold rate with several descriptive variables that tend to be measured as signs of geopolitical and economical disasters. The study has surveyed the probability of forecasting gold rates.…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

Gold Price Forecasting Using Machine Learning Techniques: Review of a Decade

Das

Nayak

Rao

et al. 2021

Advances in Intelligent Systems and Computing

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Price of the gold plays a major role in monetary as well as financial systems. Prediction and forecasting the upcoming tendency of gold prices and other valuable metals will be helpful for investors and money managers to evade choosing when to supply this commodity. Central banks throughout the globe uphold gold reserves to assure the currency holders, the money of their shareholders, and foreigndebt creditors. They also utilize the gold treasury as a means to manage inflation and toughen their country's economic standing. During this procedure, the prediction of the gold rate has become the biggest issue now a days. So, various methods, especially intelligent techniques, have played a vital role in predicting gold prices. Moreover, a comparative investigation on the impact of machine learning (ML) algorithms such as support vector machine (SVM), random forest (RF), linear regression (LR), decision tree (DT), and other hybrid methods for gold price forecasting has been made. Some significant research directions for additional research on gold price prediction are highlighted which may assist the researchers to widen proficient intelligent techniques for the prediction of gold rate.

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Section: Linear Regression (Lr)mentioning

confidence: 99%

Gold Price Forecasting Using Machine Learning Techniques: Review of a Decade

Das

Nayak

Rao

et al. 2021

Advances in Intelligent Systems and Computing

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“…SVM has been applied in various domains for price forecasting, such as crude oil [44][45][46][47], rubber [48], gold [49], and electric [50][51][52][53], agricultural [54], and stock [55] from 2005 to 2021. SVM can avoid the over-fitting problem and model the nonlinear relationships stably, as it applied the risk minimization principle in training.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…SVM can avoid the over-fitting problem and model the nonlinear relationships stably, as it applied the risk minimization principle in training. SVM has been tested and compared to statistical models such as ARIMA and has shown a better performance [47,49]. The interpreted result suggests that SVM performs better for capturing nonlinear relationships and in handling irregularities than statistical models.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

et al. 2021

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Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact on the pricing decision to advertise on those media. Various environmental factors affect the audience attention level toward advertising signage. Fixed-price strategies, which have been applied in DSA for pricing decisions, are generally inefficient at maximizing the potential profit of the service provider, as the environmental factors that could affect the audience attention are changing fast and are generally not considered in the current pricing solutions in a timely manner. Therefore, the time-series forecasting method is a suitable pricing solution for DSA, as it improves the pricing decision by modeling the changes in the environmental factors and audience attention level toward signage for optimal pricing. However, it is difficult to determine an optimal price forecasting model for DSA with the increasing number of available time-series forecasting models in recent years. Based on the 84 research articles reviewed, the data characteristics analysis in terms of linearity, stationarity, volatility, and dataset size is helpful in determining the optimal model for time-series price forecasting. This paper has reviewed the widely used time-series forecasting models and identified the related data characteristics of each model. A framework is proposed to demonstrate the model selection process for dynamic pricing in DSA based on its data characteristics analysis, paving the way for future research of pricing solutions for DSA.

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“…For more than 2000 years, gold has been known as a high-quality liquid asset that has served as a hedge and safe haven for exchanging commodities. Several robust statistical models have been proposed to explore the predictability of the gold price [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…They reported that the performance of the SVM-PSO technique is better than the SVM algorithm. The ARIMA and SVR models were employed in [ 3 ] to predict the daily price of gold, whereas in [ 30 ], the SVC model was utilized to predict whether the price of three cryptocurrencies (BTC, ETH, and LTC) would increase or decrease in the next day.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Predicting Cryptocurrency Returns Based on the Gold Prices with Support Vector Machines during the COVID-19 Pandemic Using Sensor-Related Data

Mahdi

Leiva

Mara'Beh

et al. 2021

Sensors

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In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.

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Prediction of gold price with ARIMA and SVM

Cited by 35 publications

References 9 publications

Gold Price Forecasting Using Machine Learning Techniques: Review of a Decade

Gold Price Forecasting Using Machine Learning Techniques: Review of a Decade

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

A New Approach to Predicting Cryptocurrency Returns Based on the Gold Prices with Support Vector Machines during the COVID-19 Pandemic Using Sensor-Related Data

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