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Abstract Neural network models have been successfully used to predict stock prices, weather, and traffic patterns. Due to the sensitivity of the data, it is very effective in identifying and maintaining long-term dependencies in time series. The back propagation neural network (BPNN) model works well in regression and classification applications, such as predicting stock prices and sales volumes. BPNN needs to sort out the mapping between inputs and outputs before continuous values. BPNN neural network model is integrated with ensemble empirical mode decomposition (EEMD), and a new hybrid neural network prediction model is constructed. Integrating ensemble empirical mode decomposition, collecting and preprocessing sequence features, reducing noise, improving robustness, and then training neural networks with returned feature vectors instead. In the international gold price series forecasting, the $$R^2$$ R 2 of the new hybrid model is 1.85 $$\%$$ % better than the existing EEMD-LSTM model, 3.8 $$\%$$ % and 5.44 $$\%$$ % better than the independent BPNN and long short-term memory network (LSTM) neural network models, respectively. Compared with LSTM, the BPNN plays the performance of EEMD better, reduces the error to a certain extent, and improves the prediction accuracy. |
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