Comparative Performance of Neural Network Architectures in Gold Price Prediction: a Study of GRU, N-BEATS, LSTM and Hybrid Models

Author(s)	Gonese Tapiwa Anthony, Nana Adi Appiah, Dongxiao Ren
Country	China
Abstract	This study assesses the efficacy of a variety of neural network models in predicting gold prices, with a particular emphasis on GRU, N-BEATS, N-BEATS-GRU, N-BEATS-LSTM, and LSTM. We evaluated the models using a comprehensive dataset and metrics such as MSE, MAE, MAPE, and RMSE. The GRU model demonstrated the highest MAPE of 3.10%, as indicated by the results. N-BEATS was the second-best-performing model. In stark contrast to expectations, hybrid models, notably N-BEATS-LSTM, underperformed. Compared to LSTM-based models, visual analysis demonstrated that GRU and N-BEATS more effectively captured short-term and long-term trends. The results indicate that simplified models, such as GRU, may be more effective than complex hybrids in predicting gold prices.
Keywords	Gold Price Prediction, Time Series Forecasting, Neural Networks, GRU, N-BEATS, LSTM, Hybrid Models
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-07-06
Cite This	Comparative Performance of Neural Network Architectures in Gold Price Prediction: a Study of GRU, N-BEATS, LSTM and Hybrid Models - Gonese Tapiwa Anthony, Nana Adi Appiah, Dongxiao Ren - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.24040
DOI	https://doi.org/10.36948/ijfmr.2024.v06i04.24040
Short DOI	https://doi.org/gt3xnb

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