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Prediction of national agricultural products wholesale price index in China using deep learning

Abstract

The national agricultural products wholesale price index (NPI), as a main statistical indicator to reflect and evaluate the states of agricultural products wholesale market in China, can help people keep better track of agricultural products wholesale price changes and regular pattern dynamically. However, the compilation task of NPI is complicated, difficult, labor-consuming and error-prone. Thus, a dual-stage attention-based recurrent neural network (DA-RNN) model is introduced in this work to build a deep learning model for predicting NPI based on the available average prices of major agricultural products. The root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to evaluate the forecasting performance. Experimental results show that the DA-RNN model achieves the superior performance on various evaluation metrics (RMSE = 0.5892, MAE = 0.3604 and MAPE = 0.3091) compared with other deep learning methods.

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Acknowledgements

This work was supported by the Public Welfare Industry (Agriculture) Research Projects Level-2 under Grant 201503116-04-06; Postdoctoral Foundation of Heilongjiang Province under Grant LBHZ15020; Harbin Applied Technology Research and Development Program under Grant 2017RAQXJ096; and National Key Application Research and Development Program in China under Grant 2018YFD0300105-2.

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Correspondence to Qiufeng Wu.

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The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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The codes used during the current study are available from the corresponding author on reasonable request.

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Ji, M., Liu, P., Deng, Z. et al. Prediction of national agricultural products wholesale price index in China using deep learning. Prog Artif Intell (2021). https://doi.org/10.1007/s13748-021-00264-0

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Keywords

  • Time series prediction
  • Agricultural products
  • Price index
  • Dual-stage attention-based recurrent neural network
  • Deep learning