Improved Gold Price Prediction Based on the LSTM-ARIMA Hybrid Model
Yucheng Ye · Applied and Computational Engineering · 2025
Gold price forecasting is a crucial task in financial markets due to gold's unique attributes as a commodity, precious metal, and currency. Traditional time series models such as ARIMA are effective in capturing linear trends and seasonal components but struggle with nonlinear dependencies present in gold price data. In contrast, deep learning models, especially Long Short-Term Memory (LSTM) networks, excel at modeling complex nonlinear relationships and long-term dependencies. However, standalone LSTM models may overlook certain linear patterns. This paper proposes a novel hybrid forecasting approach that integrates LSTM and ARIMA models to leverage the strengths of both methodologies. The LSTM model first learns nonlinear features from historical gold price data, and the ARIMA model further models the residuals to capture remaining linear trends. Empirical analysis is conducted using daily gold price data from August 19, 2013, to November 22, 2024. Experimental results demonstrate that the hybrid LSTM-ARIMA model significantly outperforms the standalone LSTM model across all major evaluation metrics, with remarkable reductions in forecasting errors and improvements in accuracy and robustness. The proposed hybrid model offers a more reliable and precise tool for gold price prediction, providing valuable quantitative support for investors and policymakers in the gold market.