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Progress in Artificial Intelligence

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23-10-2021 | Regular Paper

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

Authors: Miaomiao Ji, Peng Liu, Zhao Deng, Qiufeng Wu

Published in: Progress in Artificial Intelligence | Issue 1/2022

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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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Title: Prediction of national agricultural products wholesale price index in China using deep learning
Authors: Miaomiao Ji
Peng Liu
Zhao Deng
Qiufeng Wu
Publication date: 23-10-2021
Publisher: Springer Berlin Heidelberg
Published in: Progress in Artificial Intelligence / Issue 1/2022
Print ISSN: 2192-6352
Electronic ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-021-00264-0

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