Wholesale Food Price Index Forecasts with the Neural Network

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Computational Intelligence and Applications Pub Date : 2023-08-08 DOI:10.1142/s1469026823500244
Xiaojie Xu, Yun Zhang
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引用次数: 2

Abstract

Food price forecasts in the agricultural sector have always been a vital matter to a wide variety of market participants. In this work, we approach this forecast problem for the weekly wholesale food price index in the Chinese market during a 10-year period of January 1, 2010–January 3, 2020. To facilitate the analysis, we propose the use of the nonlinear auto-regressive neural network. Technically, we investigate forecast performance, based upon the relative root mean square error (RRMSE) as the evaluation metrics, corresponding to one hundred and twenty settings that cover different algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data. Our experimental result suggests the construction of the neural network with three delays and 10 hidden neurons, which is trained through the Levenberg–Marquardt algorithm, as the forecast model. It leads to high accuracy and stabilities with the RRMSEs of 1.93% for the training phase, 2.16% for the validation phase, and 1.95% for the testing phase. Comparisons of forecast accuracy between the proposed model and some other machine learning models, as well as traditional time-series econometric models, suggest that our proposed model leads to statistically significant better performance. Our results could benefit different forecast users, such as policymakers and various market participants, in policy analysis and market assessments.
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基于神经网络的食品批发价格指数预测
农业部门的粮食价格预测一直是各种市场参与者的重要事项。在这项工作中,我们研究了2010年1月1日至2020年1月3日这10年期间中国市场每周食品批发价格指数的预测问题。为了便于分析,我们建议使用非线性自回归神经网络。从技术上讲,我们基于相对均方根误差(RRMSE)作为评估指标来研究预测性能,对应于120个设置,这些设置涵盖了模型估计的不同算法、隐藏神经元和延迟的数量以及数据分割的比率。我们的实验结果表明,构建了具有三个延迟和10个隐藏神经元的神经网络,通过Levenberg–Marquardt算法进行训练,作为预测模型。它具有较高的准确性和稳定性,训练阶段的RRMSE为1.93%,验证阶段为2.16%,测试阶段为1.95%。将所提出的模型与其他一些机器学习模型以及传统的时间序列计量经济模型之间的预测精度进行比较,表明我们提出的模型在统计上显著提高了性能。我们的结果可以在政策分析和市场评估中惠及不同的预测用户,如决策者和各种市场参与者。
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来源期刊
CiteScore
2.90
自引率
0.00%
发文量
25
期刊介绍: The International Journal of Computational Intelligence and Applications, IJCIA, is a refereed journal dedicated to the theory and applications of computational intelligence (artificial neural networks, fuzzy systems, evolutionary computation and hybrid systems). The main goal of this journal is to provide the scientific community and industry with a vehicle whereby ideas using two or more conventional and computational intelligence based techniques could be discussed. The IJCIA welcomes original works in areas such as neural networks, fuzzy logic, evolutionary computation, pattern recognition, hybrid intelligent systems, symbolic machine learning, statistical models, image/audio/video compression and retrieval.
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