PDF(Pubmed)
Abstract：
The vegetable sector is a vital pillar of society and an indispensable part of the national economic structure. As a significant segment of the agricultural market, accurately forecasting vegetable prices holds significant importance. Vegetable market pricing is subject to a myriad of complex influences, resulting in nonlinear patterns that conventional time series methodologies often struggle to decode. In this paper, we exploit the average daily price data of six distinct types of vegetables sourced from seven key wholesale markets in Beijing, spanning from 2009 to 2023. Upon training an LSTM model, we discovered that it exhibited exceptional performance on the test dataset. Demonstrating robust predictive performance across various vegetable categories, the LSTM model shows commendable generalization abilities. Moreover, LSTM model has a higher accuracy compared to several machine learning methods, including CNN-based time series forecasting approaches. With R2 score of 0.958 and MAE of 0.143, our LSTM model registers an enhancement of over 5% in forecast accuracy relative to conventional machine learning counterparts. Therefore, by predicting vegetable prices for the upcoming week, we envision this LSTM model application in real-world settings to aid growers, consumers, and policymakers in facilitating informed decision-making. The insights derived from this forecasting research could augment market transparency and optimize supply chain management. Furthermore, it contributes to the market stability and the balance of supply and demand, offering a valuable reference for the sustainable development of the vegetable industry.
摘要：
蔬菜部门是社会的重要支柱，也是国民经济结构中不可或缺的组成部分。作为农业市场的重要组成部分，准确预测蔬菜价格具有重要意义。蔬菜市场定价受到无数复杂的影响，导致传统时间序列方法经常难以解码的非线性模式。在本文中,我们利用来自北京七个主要批发市场的六种不同类型蔬菜的平均每日价格数据，从2009年到2023年。在训练LSTM模型时，我们发现它在测试数据集上表现出卓越的性能。展示各种蔬菜类别的强大预测性能，LSTM模型显示出值得称赞的泛化能力。此外，与几种机器学习方法相比，LSTM模型具有更高的精度，包括基于CNN的时间序列预测方法。由于R2评分为0.958，MAE为0.143，我们的LSTM模型相对于传统机器学习模型在预测准确性方面提高了5%以上。因此,通过预测未来一周的蔬菜价格，我们设想这个LSTM模型在现实世界中的应用来帮助种植者，消费者,和政策制定者促进知情决策。从这项预测研究中得出的见解可以提高市场透明度并优化供应链管理。此外，它有助于市场稳定和供需平衡，为蔬菜产业的可持续发展提供有价值的参考。
