Gold price prediction using radial basis function neural network

Hussein, Shamsul Faisal Mohd; Shah, Mohd Badril Nor; Jalal, Mohd Razi Abd; Abdullah, Shahrum Shah

doi:10.1109/icmsao.2011.5775457

Cited by 24 publications

(12 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Their estimate depended on the recorded costs of the stock file and gold costs. Malliaris et al [2] utilized occasions arrangement procedures and Artificial Neural Networks for gauging the costs of gold, oil and Euro. They gave an interrelationship among the three and proposed ANN procedure to estimate the individual factors.…”

Section: Literature Surveymentioning

confidence: 99%

Gold Price Prediction using Eight Neighborhood Non Linear Cellular Automata

Sreee*,

Ramu

2019

IJITEE

View full text Add to dashboard Cite

Gold price prediction is pronounced as one of the dynamic problems addressed by many researchers. Several researchers have proposed various methods to predict the gold price; still there is a potential room to a new novel method with more accuracy and precise prediction. This paper proposes a supervised classifier with an Eight Neighborhood -Non Linear Cellular Automata to predict the exact gold price. The input for this proposed classifier is taken from the time series data of the last ten years in India. The classifier is trained and tested to give daily predictions to the users, which helps many investors to decide the time to buy or sell gold. This classifier is trained to process large amount of statistical data, process the business decisions listed in news articles. Based on all these parameters it predicts the gold variations in future. The accuracy of the classifier tested with standard datasets was reported as 86.7%, which is considerably better when compared with the existing literature

show abstract

Section: Literature Surveymentioning

confidence: 99%

Gold Price Prediction using Eight Neighborhood Non Linear Cellular Automata

Sreee*,

Ramu

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…b) Is determined by how much of past data that will be used to predict the future (Hussein et al, 2011).…”

Section: Determining Inputmentioning

confidence: 99%

Forecasting of PT. PLN (Persero) revenue using radial basis function neural network

Fajarianti¹,

Suprijadi²,

Franty³

2017

AIP Conference Proceedings

View full text Add to dashboard Cite

Abstract. PT. PLN (Persero) Distribusi Jakarta Raya (Disjaya) is a government-owned company that job is to maintain electricity distribution in Jakarta and Tangerang. The company's revenue can be seen from the pattern of the existing data, constantly increasing every year. This research aims to forecast company's revenue. The forecasting method using Artificial Neural Network method with Radial Basis Function (RBF) model based on historic data from January 2010 to December 2015. Based on the result of this research, the best model obtained (1-6-1) with composition 1 Neuron from input layer, 6 Neuron from hidden layer, and 1 Neuron output layer. The MAPE obtained with this model is 1.32 %.

show abstract

“…The hybrid model of Genetic Algorithm and Neural network (GA-BP) has been proposed by Chunmei [3], and the experimental results indicated that the prediction model yields acceptable results. The radial basis function (RBF) neural network has been presented by Hussein et al [4]. However, the result obtained is not promising.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, this research will combine the opinion scores obtained from the news release and conventional variables used frequently by researchers to enhance the predictability of the gold prices forecasting. Conventionally, gold price forecasting studies consider only gold spot or gold futures as variables [3][4][5][20][21][22]. However, some scholars [15,19,23,24] believe that additional factors are also associated with gold price fluctuation, such as stock index, U.S. dollar exchange rate, and crude oil prices.…”

Section: Introductionmentioning

confidence: 99%

Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices

Yuan¹,

Lee²,

Chiu³

2020

IJCIS

View full text Add to dashboard Cite

Gold price prediction has long been a crucial and challenging research topic for gold investors. In conventional models, most scholars have used the historical gold price or economic indicators to forecast gold prices. The gold prices depend mainly on confidence in the current market. To reduce the time delay of economic indicators in this study, the daily online global gold news undergoes a text mining approach. An opinion score is generated by ascertaining the opinion polarity and words in the daily gold news. The opinion score represents the current market trends and used as an input predictor in the forecasting model. Subsequently, the least square support vector regression (LSSVR) that is optimized by the genetic algorithm (GA) is employed to train and predict the future gold price. The mean absolute percentage error (MAPE) is adopted to evaluate the model performance. This study is the first to use the opinion score through text mining as an input predictor to GA-LSSVR in forecasting gold prices. The experiment results demonstrate that the input predictor, opinion score, can improve the predicting ability of GA-LSSVR model in terms of MAPE.

show abstract

Gold price prediction using radial basis function neural network

Cited by 24 publications

References 3 publications

Gold Price Prediction using Eight Neighborhood Non Linear Cellular Automata

Gold Price Prediction using Eight Neighborhood Non Linear Cellular Automata

Forecasting of PT. PLN (Persero) revenue using radial basis function neural network

Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices

Contact Info

Product

Resources

About