Application of the Average Based Fuzzy Time Series Lee Method for Forecasting World Gold Prices

Husnul Khotimah; Qurratul Aini; Nur Asmita Purnamasari

doi:10.29303/emj.v7i2.237

Authors

Husnul Khotimah Universitas Mataram
Qurratul Aini Universitas Mataram
Nur Asmita Purnamasari Universitas Mataram

DOI:

https://doi.org/10.29303/emj.v7i2.237

Keywords:

Fuzzy Time Series Lee, Gold, Mean Absolute Percentage Error

Abstract

Gold is a investment that investors are interested in because it has relatively low risk and gold investment is not affected by inflation. Gold prices always change from time to time, so it is necessary to forecast gold prices as a basis for investors in making decisions. The forecasting method used in the fuzzy time series lee method. The purpose of this research is determine the world prices and determine the accuracy of the gold price forecasting value ortained using fuzzy time series lee method. The results of this research are forecasting gold prices in the period November 20, 2023 of US$ 63,89/grams and relatively the level of forecasting accuracy based on MAPE value of 0,540091% included in the very good criteria in forecasting gold prices.

References

Aditya, S., Devianto, D., & Maiyastri. (2019). Forecasting Indonesian Gold Prices Using the Classic Fuzzy Time Series Method. UNAND Mathematics Journal. 8(2): 45-52. https://doi.org/10.25077/jmu.8.2.45-52.2019

Elfajar, A. B., Setiawan, B. D., & Dewi, C. (2018). Forecasting the Number of Tourist Visits to Batu City Using the Invariant Fuzzy Time Series Method. Journal of Information Technology and Computer Science Development. 2(3): 1283-1289. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/19

Fauzan, M. (2020). Analysis of World Gold Price Forecasting Using Fuzzy Time Series Cheng (Case Study: World Gold Prices for the Period January 2010 – December 2019). Yogyakarta. UII.

Husnan, Suad. (2000). Financial Management Theory and Application (Long Term Decisions) Book 1. Yogyakarta: BPFE.

Investing. (2023). Xau US$ Historical Data. Accessed from https://id.investing.com/currencies/xau-US$-historical-data on November 17 2023.

Sudjana. (1996). Statistical Methods. Bandung: PT. Tarsito.

Sunariyah. (2003). Introduction to Capital Market Knowledge, third edition. UPP-AMP YKPN. Yogyakarta.

Qiu, W., Liu, X., & Li, H. (2011). A Generalized Method for Forecasting Based on Fuzzy Time Series. International Journal of Expert Systems with Applications. 38 : 10446-10453. https://doi.org/10.1016/j.eswa.2011.02.096

Wang, Y., Lei, Y.,& Fan, X. (2015). Intuitionistic Fuzzy Time Series Forecasting Model Based on intuitionistis Fuzzy Reasoning. International Journal of Mathematical Problems in Enginering. 2016(1): 1-12. https://doi.org/10.1155/2016/5035160