Department of Logistics Management and Engineering, Nanning Normal University, Nanning, Guangxi, China.
Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, Guangxi, China.
PLoS One. 2023 Apr 13;18(4):e0283584. doi: 10.1371/journal.pone.0283584. eCollection 2023.
Accurate product price forecasting is helpful for scientific decision-making and precise industrial planning. As a characteristic fruit that drives regional development, mango price prediction is of great significance to several economies. However, owing to the strong volatility of mango prices, forecasting is vulnerable to uncertainties and is very challenging. In this study, a deep-learning combination forecasting model based on a back-propagation (BP) long short-term memory (LSTM) neural network is proposed. Using daily mango price data from a large fruit wholesale trading center in China from January 2nd, 2014, to April 18th, 2022, mango price changes are learned and predicted to support the fruit industry. The results show that the root mean-square error, mean absolute percentage error, and the R2 determination coefficient of the BP-LSTM combination model are 0.0175, 0.14%, and 0.9998, respectively. The prediction results of the combined model are better than those of the separate BP and LSTM models. Furthermore, it best fits the actual price profile and has better generalizability.
准确的产品价格预测有助于科学决策和精确的产业规划。芒果作为推动区域发展的特色水果，其价格预测对多个经济体具有重要意义。然而，由于芒果价格波动剧烈，预测容易受到不确定性的影响，因此极具挑战性。在本研究中，提出了一种基于反向传播（BP）长短时记忆（LSTM）神经网络的深度学习组合预测模型。利用中国某大型水果批发市场 2014 年 1 月 2 日至 2022 年 4 月 18 日的每日芒果价格数据，学习和预测芒果价格变化，为水果产业提供支持。结果表明，BP-LSTM 组合模型的均方根误差、平均绝对百分比误差和 R2 决定系数分别为 0.0175、0.14%和 0.9998，组合模型的预测结果优于单独的 BP 和 LSTM 模型。此外，该模型最贴合实际价格走势，具有更好的泛化能力。
