Subject Areas : Computer Engineering
1 - Department of Accounting, Azadshahr Branch, Islamic Azad University, Azadshahr, Iran.
Keywords:
Abstract :
[1] J. Białkowski,, M.T. Bohl, , P.M. Stephan, and T.P. Wisniewski, T.P., "The gold price in times of crisis", International Review of Financial Analysis, Vol.41, pp.329-339, 2015.
[2] E. Jianwei, J. Ye, and H. Jin, "A novel hybrid model on the prediction of time series and its application for the gold price analysis and forecasting. Physica A: Statistical Mechanics and its Applications", Vol. 527, p.121454, 2019.
[3] F. Zhou, Z. Huang, and C. Zhang. "Carbon price forecasting based on CEEMDAN and LSTM", Applied energy, Vol. 311, p.1, 2022.
[4] K. Wang, X. Qi, H. Liu, and J. Song," Deep belief network based k-means cluster approach for short-term wind power forecasting", Energy, Vol. 165, pp.840-852, 2018.
[5] P.A. Abken, "The economics of gold price movements", 1980.
[6] J. N. Fortune, "The inflation rate of the price of gold, expected prices and interest rates", Journal of Macroeconomics, Vol. 9, No. 1, pp.71-82, 1987.
[7] G. Grudnitski, and L. Osburn," Forecasting S&P and gold futures prices: An application of neural networks", Journal of Futures Markets, Vol.13, No.6, pp.631-643, 1993.
[8] A. Paris, F. Parisi, and D. Díaz, " Forecasting gold price changes: Rolling and recursive neural network models", Journal of Multinational financial management, Vol.18, No.5, pp.477-487, 2008.
[9] A. Yazdani-Chamzini, S.H. Yakhchali, D. Volungevicˇiene˙, and E. K. Zavadskas. "Forecasting gold price changes by using adaptive network fuzzy inference system", Journal of Business Economics and Management, Vol.13, No.5, pp.994–1010, 2012.
[10] VE. Salis, A. Kumari, A. Singh, " Prediction of gold stock market using hybrid approach. In: Emerging research in electronics", computer science and technology, Springer, pp. 803–812, 2019.
[11] F. Weng, Y. Chen, Z. Wang, M. Hou, J. Luo, and Z. Tian, "Gold price forecasting research based on an improved online extreme learning machine algorithm", Journal of Ambient Intelligence and Humanized Computing, Vol. 11, pp.4101-4111, 2020.
[12] I.E. Livieris, E. Pintelas, and P. Pintelas, " A CNN–LSTM model for gold price time-series forecasting", Neural computing and applications, Vol.32, pp.17351-17360, 2020.
[13] E. Jianwei, J. Ye, and H. Jin, H.," A novel hybrid model on the prediction of time series and its application for the gold price analysis and forecasting", Physica A: Statistical Mechanics and its Applications, Vol. 527, p.121454, 2019.
[14] M. Risse, "Combining wavelet decomposition with machine learning to forecast gold returns", International Journal of Forecasting, Vol.35, No.2, pp.601-615, 2019.
[15] L. Xian, K. He, and K.K.Lai, K.K., "Gold price analysis based on ensemble empirical model decomposition and independent component analysis", Physica A: Statistical Mechanics and its Applications, Vol. 454, pp.11-23, 2016.
[16] Y. Liang, Y. Lin, and Q. Lu, " Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM", Expert Systems with Applications, Vol.206, pp.117847, 2022.
[17] N.E. Huang, Z. Shen, S. R. Long, M. C. Wu, H.H. Shih, Q. Zheng, N. C. Yen, C.C. Tung, and H.H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, Vol.454, pp.903-995, 1998.
[18] Z.Wu, and N. E. Huang, "Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis, Vol.1, No.1, pp.1-41,2009.
[19] J.R. Yeh, J.S. Shieh, and N.E. Huang," Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method", Adv Adapt Data Anal , Vol.2, pp.135–156, 2010.
[20] X. Li, and C. Li, "Improved CEEMDAN and PSO-SVR modeling for near-infrared noninvasive glucose detection",Computational and mathematical methods in medicine, 2016.
[21] M.E. Torres, M. A. Colominas, G. Schlotthauer, and P. Flandrin, "A complete ensemble empirical mode decomposition with adaptive noise", IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4144-4147), 2011.
[22] M. A. Colominas, G. Schlotthauer, and M.E.Torres, "Improved complete ensemble EMD: A suitable tool for biomedical signal processing. Biomedical Signal Processing and Control, Vol.14, pp.19-29, 2014.
[23] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, " Learning phrase representations using RNN encoder-decoder for statistical machine translation.", available online: https://arxiv.org/pdf/1406.1078.pdf, 2014.