Gold Price Prediction using Machine Learning and Deep Learning Algorithms
- سال انتشار: 1402
- محل انتشار: سیزدهمین کنفرانس بین المللی تحقیقات پیشرفته در علوم، مهندسی و فناوری
- کد COI اختصاصی: RSETCONF14_014
- زبان مقاله: انگلیسی
- تعداد مشاهده: 58
نویسندگان
PhD in computer science-soft computing and artificial intelligence, Islamic Azad University, South Tehran branch
PhD student in computer engineering - network and computing, Islamic Azad University, South Tehran branch
PhD student in computer engineering - network and computing, Islamic Azad University, South Tehran branch
PhD student in Computer Engineering-Artificial Intelligence, Islamic Azad University, South Tehran Branch
چکیده
Today, different markets and economic sectors are directly or indirectly affected by gold price; thus, its prediction is a big challenge for both investors and researchers. On the other hand, the nonstationary and nonlinear patterns of gold price data cause the prediction process even more complex. To address this challenge, a hybrid model was developed in this paper to predict gold price, with a concentration on enhancing accuracy through considering the gold price data characteristics. To do this, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Gated recurrent units (GRU) were used to deal with the nonstationary and nonlinear nature of the gold price data. The former was first applied to the decomposition of time-series data of gold price into a number of components. Then, GRU was applied to the prediction of the components. To end with, all the components’ prediction results were summed up to attain the final prediction result. The efficiency of the developed model was evaluated using real-world gold data, which confirmed its superiority over the standard methods used for comparison.کلیدواژه ها
Deep learning, Decomposition, Forecasting, Gold Priceاطلاعات بیشتر در مورد COI
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