Citation

Leow, Meng Chew and Ngu, Stephen Chuan Yi and Ong, Lee Yeng (2023) Gold Prices Forecasting Using Bidirectional LSTM Model Based on SPX500 Index, USD Index, Crude Oil Prices and CPI. In: 2023 11th International Conference on Information and Communication Technology (ICoICT), 23-24 August 2023, Melaka, Malaysia.

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Abstract

The objective of this research is to develop an accurate gold price forecasting model using Bidirectional LSTM model, taking into account significant factors such as the SPX500 Index, USD Index, Crude Oil Prices, and Consumer Price Index (CPI). Previous studies suggested the potential for improved performance when utilizing a Bidirectional LSTM model. To optimize the models, a random search tuner was employed to identify the best hyperparameters for Bidirectional LSTM model. The results demonstrate that the Bidirectional LSTM model exhibited a marked potential for enhancing the accuracy of gold price predictions. This advanced forecasting model can provide valuable insights for governments, policymakers, and investors, empowering them to make well-informed decisions in the gold market.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Bidirectional LSTM, SPX500 Index, USD Index, Crude Oil Prices, Consumer Price Index, Gold Prices Forecasting, Time-series analysis.
Subjects:	H Social Sciences > HB Economic theory. Demography > HB221-236 Price
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Oct 2023 08:23
Last Modified:	31 Oct 2023 08:23
URII:	http://shdl.mmu.edu.my/id/eprint/11801

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