Alameer, Z., Elaziz, M. A., Ewees, A. A., Ye, H., & Jianhua, Z. (2019). Forecasting copper prices using hybrid adaptive neuro-fuzzy inference system and genetic algorithms. Natural Resources Research, 28, 1385-1401.
Basistha, A., Kurov, A., & Wolfe, M. H. (2015). Forecasting commodity price volatility with internet search activity.
Bicchal, M., & Raja Sethu Durai, S. (2019). Rationality of inflation expectations: an interpretation of Google Trends data. Macroeconomics and Finance in Emerging Market Economies, 12(3), 229-239.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees (Wadsworth International Group, Belmont, California, 1984). Google Scholar.
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
Bulut, L. (2018). Google Trends and the forecasting performance of exchange rate models. Journal of Forecasting, 37(3), 303-315.
Buncic, D., & Moretto, C. (2015). Forecasting copper prices with dynamic averaging and selection models. The North American Journal of Economics and Finance, 33, 1-38.
Carta, S., Medda, A., Pili, A., Reforgiato Recupero, D., & Saia, R. (2018). Forecasting e-commerce products prices by combining an autoregressive integrated moving average (ARIMA) model and google trends data. Future Internet, 11(1), 5.
Castán-Lascorz, M. A., Jiménez-Herrera, P., Troncoso, A., & Asencio-Cortés, G. (2022). A new hybrid method for predicting univariate and multivariate time series based on pattern forecasting. Information Sciences, 586, 611-627.
Challet, D., & Ayed, A. B. H. (2014). Do Google Trend data contain more predictability than price returns?. arXiv preprint arXiv:1403.1715.
Chang, J. H., & Tseng, C. Y. (2019). Analyzing google trends with travel keyword rankings to predict tourists into a group. Journal of Internet Technology, 20(1), 247-256.
Chen, Y., He, K., & Zhang, C. (2016). A novel grey wave forecasting method for predicting metal prices. Resources Policy, 49, 323-331.
Cortez, C. T., Saydam, S., Coulton, J., & Sammut, C. (2018). Alternative techniques for forecasting mineral commodity prices. International Journal of Mining Science and Technology, 28(2), 309-322.
Dehghani, H. (2018). Forecasting copper price using gene expression programming. Journal of Mining and Environment, 9(2), 349-360.
Deng, H., Fannon, D., & Eckelman, M. J. (2018). Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata. Energy and Buildings, 163, 34-43.
Dhiyanji, M., & Sundaravadivu, K. (2016). Application of soft computing technique in the modelling and prediction of gold and silver rates. Journal of Advances in Technology and Engineering Research, 2(4), 118-124.
Díaz, J. D., Hansen, E., & Cabrera, G. (2020). A random walk through the trees: Forecasting copper prices using decision learning methods. Resources Policy, 69, 101859.
Do, Q. H., & Yen, T. T. H. (2019). Predicting primary commodity prices in the international market: an applicatıon of group method of data handling neural network. Journal of Management Information & Decision Sciences, 22(4).
Doviz.com. (2021, October, 29). Canlı Gümüş Fiyatı - Anlık Gümüş Ne Kadar? Doviz.com .Access address: https://altin.doviz.com/gumus.
Ekinci, E., & Omurca, S. İ. (2017). Ürün Özelliklerinin Konu Modelleme Yöntemi ile Çıkartılması. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 9(1), 51-58.
Elman, J. L. (1990). Finding structure in time. Cognitive science, 14(2), 179-211.
García, D., & Kristjanpoller, W. (2019). An adaptive forecasting approach for copper price volatility through hybrid and non-hybrid models. Applied Soft Computing, 74, 466-478.
Google Trends (2022, November,14). FAQ about Google Trends Data. Access address: https://support.google.com/trends/answer/4365533
Guha, B., & Bandyopadhyay, G. (2016). Gold price forecasting using ARIMA model. Journal of Advanced Management Science, 4(2).
Gupta, R., Pierdzioch, C., & Wong, W. K. (2021). A note on forecasting the historical realized variance of oil-price movements: the role of gold-to-silver and gold-to-platinum price ratios. Energies, 14(20), 6775.
Guzavicius, A. (2015). Nowcasting commodity markets using real time data stream. Procedia-Social and Behavioral Sciences, 213, 481-484.
Harper, A., Jin, Z., Sokunle, R., & Wadhwa, M. (2013). Price volatility in the silver spot market: an empirical study using Garch applications. Journal of Finance and Accountancy, 13, 1.
Huarng, K. H., & Yu, T. H. K. (2019). Application of Google trends to forecast tourism demand. Journal of Internet Technology, 20(4), 1273-1280.
Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78, 15169-15211.
Jun, S. P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological forecasting and social change, 130, 69-87.
Kamdem, J. S., Essomba, R. B., & Berinyuy, J. N. (2020). Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities. Chaos, Solitons & Fractals, 140, 110215.
Kim, J., Cha, M., & Lee, J. G. (2017). Nowcasting commodity prices using social media. PeerJ Computer Science, 3, e126.
Kocatepe, C. İ., & Yıldız, O. (2016). Ekonomik endeksler kullanılarak Türkiye’deki altın fiyatındaki değişim yönünün yapay sinir ağları ile tahmini. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 4(3), 926-934.
Kolchyna, O. (2017). Evaluating the impact of social-media on sales forecasting: a quantitative study of worlds biggest brands using Twitter, Facebook and Google Trends (Doctoral dissertation, UCL (University College London)).
Korkmaz, D., Çelik, H. E., & Kapar, M. (2018). Sınıflandırma ve regresyon ağaçları ile rastgele orman algoritması kullanarak botnet tespiti: Van Yüzüncü Yıl Üniversitesi Örneği. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23(3), 297-307.
Kriechbaumer, T., Angus, A., Parsons, D., & Casado, M. R. (2014). An improved wavelet–ARIMA approach for forecasting metal prices. Resources Policy, 39, 32-41.
Kristjanpoller, W., & Hernández, E. (2017). Volatility of main metals forecasted by a hybrid ANN-GARCH model with regressors. Expert Systems with Applications, 84, 290-300.
Li, W., Cheng, Y., & Fang, Q. (2020). Forecast on silver futures linked with structural breaks and day-of-the-week effect. The North American Journal of Economics and Finance, 53, 101192.
Liu, C., Hu, Z., Li, Y., & Liu, S. (2017). Forecasting copper prices by decision tree learning. Resources Policy, 52, 427-434.
Lu, Q., Li, Y., Chai, J., & Wang, S. (2020). Crude oil price analysis and forecasting: A perspective of “new triangle”. Energy Economics, 87, 104721.
Lyócsa, Š., & Molnár, P. (2016). Volatility forecasting of strategically linked commodity ETFs: gold-silver. Quantitative Finance, 16(12), 1809-1822.
Mellon, J. (2014). Internet search data and issue salience: The properties of Google Trends as a measure of issue salience. Journal of Elections, Public Opinion & Parties, 24(1), 45-72.
Mitra, A., & Jalan, A. K. (2014). Prediction of silver price in volatile market (USD)-based on auto regression integrated moving average. In Proceeding of the 2014 International Conference on Computing, Communication & Manufacturing (pp. 119-130).
Nagy, Z. (2018). Artificial Intelligence and Machine Learning Fundamentals: Develop real-world applications powered by the latest AI advances. Packt Publishing Ltd.
Petropoulos, A., Siakoulis, V., Stavroulakis, E., Lazaris, P., & Vlachogiannakis, N. (2022). Employing google trends and deep learning in forecasting financial market turbulence. Journal of Behavioral Finance, 23(3), 353-365.
Phitthayanon, C., & Rungreunganun, V. (2019). Material Cost Prediction for Jewelry Production Using Deep Learning Technique. Engineering Journal, 23(6), 145-160.
Pierdzioch, C., Risse, M., & Rohloff, S. (2016). A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation. Applied Economics Letters, 23(5), 347-352.
Ralmugiz, U., Wahyudi, E., & Abadi, A. M. Application of Fuzzy Systems for Predicting Silver Price.
Rasmussen, C. E., & Williams, C. K. (2006). Gaussian processes for machine learning (Vol. 1, p. 159). Cambridge, MA: MIT press.
Reeve, T. A., & Vigfusson, R. J. (2011). Evaluating the forecasting performance of commodity futures prices. FRB International Finance Discussion Paper, (1025).
Sadorsky, P. (2021). Predicting gold and silver price direction using tree-based classifiers. Journal of Risk and Financial Management, 14(5), 198.
Salisu, A. A., Ogbonna, A. E., & Adediran, I. (2021). Stock‐induced Google trends and the predictability of sectoral stock returns. Journal of Forecasting, 40(2), 327-345.
Salisu, A. A., Ogbonna, A. E., & Adewuyi, A. (2020). Google trends and the predictability of precious metals. Resources Policy, 65, 101542.
Seguel, F., Carrasco, R., Adasme, P., Alfaro, M., & Soto, I. (2015). A meta-heuristic approach for copper price forecasting. In Information and Knowledge Management in Complex Systems: 16th IFIP WG 8.1 International Conference on Informatics and Semiotics in Organisations, ICISO 2015, Toulouse, France, March 19-20, 2015. Proceedings 16 (pp. 156-165). Springer International Publishing.
Shokri, B. J., Dehghani, H., & Shamsi, R. (2020). Predicting silver price by applying a coupled multiple linear regression (MLR) and imperialist competitive algorithm (ICA). Metaheuristic Computing and Applications, 1(1), 1.
Shukor, S. A., Sufahani, S. F., Khalid, K., Abd Wahab, M. H., Idrus, S. Z. S., Ahmad, A., & Subramaniam, T. S. (2021, May). Forecasting Stock Market Price of Gold, Silver, Crude Oil and Platinum by Using Double Exponential Smoothing, Holt’s Linear Trend and Random Walk. In Journal of Physics: Conference Series (Vol. 1874, No. 1, p. 012087). IOP Publishing.
Szarek, D., Bielak, Ł., & Wyłomańska, A. (2020). Long-term prediction of the metals’ prices using non-Gaussian time-inhomogeneous stochastic process. Physica A: Statistical Mechanics and its Applications, 555, 124659.
Torbat, S., Khashei, M., & Bijari, M. (2018). A hybrid probabilistic fuzzy ARIMA model for consumption forecasting in commodity markets. Economic Analysis and Policy, 58, 22-31.
Weng, B., Lu, L., Wang, X., Megahed, F. M., & Martinez, W. (2018). Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems with Applications, 112, 258-273.
Wilcoxson, J., Follett, L., & Severe, S. (2020). Forecasting foreign exchange markets using Google Trends: Prediction performance of competing models. Journal of Behavioral Finance, 21(4), 412-422.
Yılmaz, H. (2014). Random forests yönteminde kayıp veri probleminin incelenmesi ve sağlık alanında bir uygulama (Master's thesis, Eskişehir Osmangazi Üniversitesi).
Zhao, L. T., Guo, S. Q., Miao, J., & He, L. Y. (2020). How Does Internet Information Affect Oil Price Fluctuations? Evidence from the Hot Degree of Market. Discrete Dynamics in Nature and Society, 2020, 1-18.
Zhao, T. (2021, October). Computer Intelligent Volatility Forecast Method for Silver by Autoregressive Moving Average and HAR-Type Models. In 2021 IEEE International Conference on Data Science and Computer Application (ICDSCA) (pp. 556-565). IEEE.
Zhu, Q., Zhang, F., Liu, S., Wu, Y., & Wang, L. (2019). A hybrid VMD–BiGRU model for rubber futures time series forecasting. Applied Soft Computing, 84, 105739.