Gold Price Prediction using Machine Learning and Deep Learning Algorithms
Publish place: 13th International Engineering Conference on Advanced Research in Science and Technology
Publish Year: 1402
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
RSETCONF14_014
تاریخ نمایه سازی: 15 اسفند 1402
Abstract:
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.
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Authors
Mozhdeh Tanha
PhD in computer science-soft computing and artificial intelligence, Islamic Azad University, South Tehran branch
Siavash Siavashian Rashidi
PhD student in computer engineering - network and computing, Islamic Azad University, South Tehran branch
Soheila Estaji
PhD student in computer engineering - network and computing, Islamic Azad University, South Tehran branch
Seyyed Mojtaba Mousavi fard
PhD student in Computer Engineering-Artificial Intelligence, Islamic Azad University, South Tehran Branch