Emsemble Learning Fused with CNN-BiLSTM-Attention for Price Forecasting

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    Price forecasting is important for the stability of bulk agricultural commodity markets, but bulk agricultural commodity prices have complex correlations with multiple factors. In order to address the current problems of strong dependence on data integrity and the difficulty of single models to fully utilize multiple data features in price forecasting, a boosting ensemble learning method that combines the attention mechanism-based convolutional bi-directional long short-term memory neural network (CNN-BiLSTM-Attention), support vector regression (SVR), and LightGBM is proposed, and experiments are conducted on the datasets containing historical trades, weather, exchange rate, oil price, and other features data, respectively. The experiment takes the price forecasting of wheat and cotton as the target task, uses the mutual information method for feature selection, selects the CNN-BiLSTM-Attention model with low error as the base model, and performs boosting ensemble learning with the machine learning model through linear regression. The experimental results show that the root mean square error (RMSE) of the ensemble learning method is 12.812 and 74.356 for wheat and cotton datasets, which are 11.00%, 0.94%, 4.44%, 1.99%, 13.03%, and 4.39% lower than the three base models, respectively. The method can effectively improve the accuracy of price forecasting.

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  • Received:December 07,2022
  • Revised:January 06,2023
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  • Online: April 07,2023
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