The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner

Çelik, Ufuk; Başarır, Çağatay

doi:10.17093/alphanumeric.290381

Cited by 22 publications

(7 citation statements)

References 23 publications

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“…ANN is a modelling method that is inspired by biological neural systems and simulates the pattern recognition ability of the human brain [59,60]. An artificial neuron is the functional unit that processes the received information.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The most commonly used neural network is feed-forward network and is employed in this paper. In this neural network, the neurons of different layers are fully interconnected to each other, but the information is unidirectional starting from the input layer through hidden layers to the output layer [5,60]. The information propagation is performed by assigning initialized weights to the connections of different neurons, which can be formulized as [5]:…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The back-propagation procedure is utilized to enhance the learning capability of ANN. This procedure computes the output error between the predicted value and real target value, then feeds it back to the ANN, and then the ANN tunes the weights and biases in the network to minimize the output error [60]. The extent of weight update is controlled by a learning rate.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China

et al. 2020

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Predictive modelling of mineral prospectivity, a critical, but challenging procedure for delineation of undiscovered prospective targets in mineral exploration, has been spurred by recent advancements of spatial modelling techniques and machine learning algorithms. In this study, a set of machine learning methods, including random forest (RF), support vector machine (SVM), artificial neural network (ANN), and a deep learning convolutional neural network (CNN), were employed to conduct a data-driven W prospectivity modelling of the southern Jiangxi Province, China. A total of 118 known W occurrences derived from long-term exploration of this brownfield area and eight evidential layers of multi-source geoscience information related to W mineralization constituted the input datasets. This provided a data-rich foundation for training machine learning models. The optimal configuration of model parameters was trained by a grid search procedure and validated by 10-fold cross-validation. The resulting predictive models were comprehensively assessed by a confusion matrix, receiver operating characteristic curve, and success-rate curve. The modelling results indicate that the CNN model achieves the best classification performance with an accuracy of 92.38%, followed by the RF model (87.62%). In contrast, the RF model outperforms the rest of ML models in overall predictive performance and predictive efficiency. This is characterized by the highest value of area under the curve and the steepest slope of success-rate curve. The RF model was chosen as the optimal model for mineral prospectivity in this region as it is the best predictor. The prospective zones delineated by the prospectivity map occupy 9% of the study area and capture 66.95% of the known mineral occurrences. The geological interpretation of the model reveals that previously neglected Mn anomalies are significant indicators. This implies that enrichment of ore-forming material in the host rocks may play an important role in the formation process of wolframite and can represent an innovative exploration criterion for further exploration in this area.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China

et al. 2020

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“…Araştırmada kullanılan değişkenlerin tanımlayıcı istatistikleri analiz edilmeden önce bu değişkenlerin kullanılma sebepleri üzerinde durulmasında fayda bulunmaktadır. Petrol fiyatları ile altın arasındaki ilişkinin var olduğunu gösteren çalışmalar literatürde geniş çapta yer almaktadır (Topçu, 2010;Adem vd., 2017;Çelik & Başarır, 2017;Yüksel & Akkoç, 2016;Mombeini & Yazdani-Chamzini, 2015;Kristjanpoller & Minutolo, 2015). Literatürde yer alan çalışmaların büyük bir çoğunluğunda petrol fiyatları değişkeni olarak dünya petrol anlaşmalarının üçte ikisini elinde tutan BO (Brent Oil -Brent Petrol) esas alınmıştır.…”

Section: Veri Setiunclassified

“…Bu araştırmada da benzer şekilde altın fiyatları üzerindeki etkisi dikkate alınarak BO fiyatları bağımsız değişken olarak esas alınmıştır. (Çelik & Başarır, 2017;Yüksel & Akkoç, 2016;Kristjanpoller & Minutolo, 2015;Topçu, 2010). Araştırmada kullanılan bir başka değişken olan DXY ise Euro, Japon Yeni, Sterlin, Kanada Doları, İsveç Kronu ve İsviçre Frangı olmak üzere ABD'nin en yüksek ticaret hacmini gerçekleştirdiği altı para birimine karşı ABD dolarının değerini gösteren endeks olarak tanımlanmaktadır.…”

Section: Veri Setiunclassified

Çok Katmanlı Yapay Sinir Ağları Yöntemi ile Altın Fiyatlarının Tahmini

Söylemez

2020

Sosyoekonomi

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The accurate estimation of financial time series provides both investment opportunities and hedging financial assets to those who take positions in financial markets. In this context, it is extremely important to estimate the price of gold, which is an important investment instrument. The research aims to estimate gold prices with various input variables the day before by using artificial neural networks method. In the study, gold prices between 11.03.2014-10.31.2019 are estimated by multilayer artificial neural networks using variables of Brent oil prices, VIX index, Dow Jones Index and US Dollar index. As a result of the research, there is a model that best estimates (98,44%) gold prices.

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Predictions of steel price indices through machine learning for the regional northeast Chinese market

Jin,

2024

Neural Comput & Applic

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The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner

Cited by 22 publications

References 23 publications

Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China

Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China

Çok Katmanlı Yapay Sinir Ağları Yöntemi ile Altın Fiyatlarının Tahmini

Predictions of steel price indices through machine learning for the regional northeast Chinese market

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