• editor.aipublications@gmail.com
  • Track Your Paper
  • Contact Us
  • ISSN: 2456-866X

International Journal Of Chemistry, Mathematics And Physics(IJCMP)

Modeling Crude Oil Prices (CPO) using General Regression Neural Network (GRNN)

Rezzy Eko Caraka

International Journal of Chemistry, Mathematics And Physics(IJCMP), Vol-1,Issue-1, May - June 2017, Pages 62-67 ,

Download | Downloads : 8 | Total View : 1128


Modeling time series is often associated with the process forecasts certain characteristics in the next period. One of the methods forecasts that developed nowadays is using artificial neural network or more popularly known as aneural network. Use neural network in forecasts time series can be agood solution, but the problem is network architecture and the training method in the right direction. General Regression Neural Network (GRNN) is one of the network model radial basis that used to approach a function. GRNN including model neural network model with a solution that quickly, because it is not needed each iteration in the estimation weight. This model has a network architecture that wasa number of units in pattern layer in accordance with the number of input data. One of the application GRNN is to predict the crude oil by using a model GRNN.From the training and testing on the data obtained by the RMSE testing 1.9355 and RMSE training 1.1048.Model is good to be used to give aprediction that is quite accurate information that is shown by the close target with the output

Artificial Intelligence; CrudeOil; General Regression Neural Network; Prediction.

[1] Caraka,R.E.,Yasin,H “PrediksiProduksi Gas Bumidengan General Regression Neural Network (GRNN)”, in Proc.National Statistics Conference (SNS IV).2014, ISSN : 2087-2590 pp 270-277.
[2] Caraka,R.E.,Yasin,H., dan Prahutama, A. “Pemodelan General Regression Neural Network (GRNN) pada Data Return IndeksHargaSaham Euro 50, JurnalGaussian,vol4,no2.2015, pp.89-94,2015.
[3] Caraka,R.E.,Yasin, H, &Prahutama,A. “PemodelanGeneral Regression Neural Network (GRNN) dengan Peubah Input Data Return Untuk PeramalanIndeksHangseng, Seminar Nasional Ilmu Komputer Universitas Negeri Semarang. ISBN:978-602-71550-0-9, 2015, pp.283-288, 2014.
[4] D. E. Rumelhart, G. E. Hinton, & R. J. Williams, “Learning representation by back-propagating errors”, Nature, vol. 323, 1991, pp. 533–536.
[5] D. Tomandl, and A. Schober, “A Modified General Regression Neural Network (MGRNN) with new, efficient training algorithms as a robust ‘block box’-tool for data analysis”, Neural Networks, vol. 14, no. 6, 2001,pp. 1023–1034.
[6] E. Parzen, “On Estimation of a Probability Density Function and Mode”, Annals of Mathematical Statistics, vol. 33, 1962, pp. 1065–1076
[7] E.A. Nadaraya, “On estimating regression”, Theory Probab. Appl.,vol. 10, 1964, pp. 186-190.
[8] G. S. Watson, “Smooth regression analysis”, Sankhya, Series A, vol.26, 1964, pp. 359-372
[9] L. Breiman, W. Meisel, and E. Purcell, “Variable kernel estimates ofmultivariate densities”, Technometrics, vol. 19, pp. 135–144, 1977.
[10] Leung, M.T.,Chen,A.N., And H. Daouk. Forecasting Exchanges rates using general regression neural networks, Computers and Operation Research Vol. 27, 2000, pp:1093-1110
[11] Sprecht, D.F.A Generalized Regression Neural Network, IEEE Transactions on Neural Networks, vol.2,No.6,1991, pp.568-576
[12] T. Cacoullos, “Estimation of a Multivariable Density”, Annals of.Institute of Statistical Mathematics, vol. 18, no. 2, 1996, pp. 179–189.
[13] Yasin,H.,Caraka,R.E., Tarno, and Hoyyi,A. Prediction Of Crude Oil Prices Using Support Vector Regression (SVR) With Grid Search–Cross Validation Algortyhm. Vol.12 No.4; August. Global Journal of Pure and Applied Mathematics. Print ISSN : 0973-1768 Online ISSN: 0973-9750,2016,pp. 3009–3020