GOLD PRICESFORECASTING USING TRIPLE EXPONENTIAL METHOD
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Khairawati
Wahyu Fuadi
Rizki Ramadhansyah
Dedi Fariadi
Governments, organizations, and citizens have taken an interest in gold price fluctuations. Gold price forecasting that is accurate may effectively capture price shift tendencies and reduce the effects of gold market volatility. However, due to the multi-factor and nonlinear nature of the gold market. The triple exponential smoothing strategy is used in this study to predict the rise in a value over time since it can replicate trends and seasonal patterns. according to the gold price swings pattern and seasonal components at the same time To calculate system accuracy, the Mean Absolute Percentage Error is employed (MAPE). With alpha 0.15 and beta 0.85 as parameter values, the triple exponential smoothing (TES) approach achieves an accuracy rate of 86.93 percent and a MAPE of 12.49 percent in this study.
Alameer, Z., Elaziz, M. A., Ewees, A. A., Ye, H., & Jianhua, Z. (2019). Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm. Resources Policy, 61, 250–260. https://doi.org/10.1016/J.RESOURPOL.2019.02.014
ALTAN, A., & KARASU, S. (2019). THE EFFECT OF KERNEL VALUES IN SUPPORT VECTOR MACHINE TO FORECASTING PERFORMANCE OF FINANCIAL TIME SERIES. The Journal of Cognitive Systems, 4(1), 17–21. https://dergipark.org.tr/en/pub/jcs/570863
Ayele, A. W., Gabreyohannes, E., & Edmealem, H. (2021). Generalized Autoregressive Conditional Heteroskedastic Model to Examine Silver Price Volatility and Its Macroeconomic Determinant in Ethiopia Market. Journal of Probability and Statistics, 2021. https://doi.org/10.1155/2021/5095181
Barrow, D., Kourentzes, N., Sandberg, R., & Niklewski, J. (2021). Automatic robust estimation for exponential smoothing: Perspectives from statistics and machine learning. Expert Systems with Applications, 160, 113637. https://doi.org/10.1016/J.ESWA.2021.113637
Chen, J., Zhu, X., & Zhong, M. (2019). Nonlinear effects of financial factors on fluctuations in nonferrous metals prices: A Markov-switching VAR analysis. Resources Policy, 61, 489–500. https://doi.org/10.1016/J.RESOURPOL.2018.04.015
de Oliveira, E. M., & Cyrino Oliveira, F. L. (2018). Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy, 144, 776–788. https://doi.org/10.1016/J.ENERGY.2017.12.049
Evamelia, E., & Panjaitan, Y. (2019). ANALISIS PERANAN EMAS DAN OBLIGASI PEMERINTAH SEBAGAI SAFE HAVEN PERIODE 2014—2018. BALANCE: Jurnal Akuntansi, Auditing Dan Keuangan, 16(2), 212–236. https://doi.org/10.25170/BALANCE.V16I2.1624
FAUSTINA, R. S., AGOESTANTO, A., & HENDIKAWATI, P. (2017). MODEL HYBRID ARIMA-GARCH UNTUK ESTIMASI VOLATILITAS HARGA EMAS MENGGUNAKAN SOFTWARE R. Unnes Journal of Mathematics, 6(1), 11–24. https://doi.org/10.15294/UJM.V6I1.11681
Humphreys, D. (2017). In search of a new China: mineral demand in South and Southeast Asia. Mineral Economics 2017 31:1, 31(1), 103–112. https://doi.org/10.1007/S13563-017-0118-7
Istamar, ., Sarfiah, S. N., & Rusmijati, . (2019). ANALISIS PENGARUH HARGA MINYAK DUNIA, HARGA EMAS, DAN NILAI KURS RUPIAH TERHADAP INDEKS HARGA SAHAM GABUNGAN DI BURSA EFEK INDONESIA TAHUN 1998-2018. DINAMIC : Directory Journal of Economic, 1(4), 433–442. https://doi.org/10.31002/DINAMIC.V1I4.805
Juhro, S. M., & Iyke, B. N. (2021). Consumer confidence and consumption expenditure in Indonesia. Economic Modelling, 89, 367–377. https://doi.org/10.1016/J.ECONMOD.2019.11.001
Majid, R. (Rumana ). (2018). Advances in Statistical Forecasting Methods: An Overview. Economic Affairs, 63(4), 295479. https://doi.org/10.30954/0424-2513.4.2018.5
Prananingtyas, P. (2018). PERLINDUNGAN HUKUM TERHADAP INVESTOR EMAS. Masalah-Masalah Hukum, 47(4), 430–444. https://doi.org/10.14710/MMH.47.4.2018.430-444
Qasim, T. B., Iqbal, G. Z., Hassan, M. U., & Ali, H. (2021). Application of Markov Regime Switching Autoregressive Model to Gold Prices in Pakistan. Review of Economics and Development Studies, 7(3), 309–323. https://doi.org/10.47067/reads.v7i3.368
Zhang, P., & Ci, B. (2021). Deep belief network for gold price forecasting. Resources Policy, 69, 101806. https://doi.org/10.1016/J.RESOURPOL.2021.101806
