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S. Sakha
S. Boonthiem
W. Klongdee



Authors and WSEAS

S. Sakha
S. Boonthiem
W. Klongdee


WSEAS Transactions on Business and Economics


Print ISSN: 1109-9526
E-ISSN: 2224-2899

Volume 15, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Volume 15, 2018


Forecasting Gold Prices Using Membership Function Model as Probability Density Function of Normal Distribution

AUTHORS: S. Sakha, S. Boonthiem, W. Klongdee

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ABSTRACT: In this paper, the new forecasting algorithm for predicting gold prices using concept of fuzzy logic with membership function model as probability density function of normal distribution is proposed and compared to the triangular membership function model and the trapezoidal membership function model. The daily gold prices from January 2015 to May 2018 collected from the London Bullion Market Association are used in this study. The prices are transformed to the rate of return and then the Train : Test percent ratio is used to obtain the training data and the testing data, respectively, and consists of 99:01, 95:05, 90:10, 80:20, 70:30, 60:40, 50:50, 40:50, 30:70, 20:80 and 10:90. The root mean squared error and the mean absolute error are applied to measure the performance of the proposed algorithm and to compare three presented models. The experimental results can conclude that the proposed algorithm can be practical to the gold price forecasting; furthermore, the proposed algorithm with the membership function model as probability density function of normal distribution can better improve itself than other models when it has more training data as well as it can demonstrate better performance than others

KEYWORDS: Fuzzy logic, gold price forecasting, probability density function of normal distribution, trapezoidal membership function, triangular membership function

REFERENCES:

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[2] R. Gupta, S. Hammoudeh, W. J. Kim, and B. D. Simo-Kengne. “Forecasting China‟s foreign exchange reserves using dynamic model averaging: The role of macroeconomic fundamentals, financial stress and economic uncertainty,” N. Am. Journ. of Econ. and Fin., vol. 28, pp. 170–189. Feb. 2014.

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[4] H. A. Sturges. “The Choice of a Class Interval,” Journ. of the Am. Statis., vol. 21, No. 153, pp. 65–66. Mar. 1926.

[5] R. J. Hyndman. (1995, January). The problem with Sturges‟ rule for constructing histograms. Available: https://robjhyndman.com/publications/sturges/

[6] C. Altavilla, and P. de Grauwe. “Nonlinearities in the relation between the exchange rate and its fundamentals,” Int.l Journ. of Fin. and Econ., vol. 15, No. 1. pp. 1–12. 2010.

[7] C. Beneki, and E. S. Silva. “Analysing and Forecasting European Union Energy Data,” Int. Journ. of Energy and Statis., pp. 1–16. Jun. 2013.

WSEAS Transactions on Business and Economics, ISSN / E-ISSN: 1109-9526 / 2224-2899, Volume 15, 2018, Art. #33, pp. 340-347


Copyright Β© 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0