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Pu'er tea is a Yunnan geographical indication product, and its brand value ranks first in China. At present, qualitative and quantitative methods with low prediction accuracy are used to predict price. In this paper, based on the current situation and industry characteristics, a differential autoregressive integrated moving average model (ARIMA) is used to predict the short-term price. From the perspective of macro and micro, back-propagation neural network model (BP) was established to predict the long-term price based on the weight ranking of 16 factors affecting the price by technique for order preference by similarity to ideal solution method (TOPSIS). The future price is predicted and analyzed, and then based on the empirical results, suggestions are put forward for the industry in terms of reducing production capacity, increasing consumer demand and combining with the publicity and promotion of Internet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021.


Zhi-Wu Dou, Ming-Xin Ji, Man Wang, Ya-Nan Shao. Price Prediction of Pu'er tea based on ARIMA and BP Models. Neural computing & applications. 2022;34(5):3495-3511

PMID: 33746365

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