Application of Weighted Regression for the Prediction of Soft Wheat Production in France

Xiangtuo Chen, Benoit Bayol, P. Cournède
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引用次数: 2

Abstract

An accurate prediction of the production level for certain individual crops is always an important topic for the crop sector and the government decision-makers. From a perspective of the global market, these statistics are needed to make accurate price predictions, which in turn serve to make business decisions. With the development of computer science and mathematics and the easier access to the open agricultural datasets, the statistical learning methods can serve as an alternative for this purpose. In this article, the weighted statistical learning methods will be applied to predict the soft wheat production in France for the period 1995-2010 with the related methodological records. In term of prediction error, the weighted regression methods are proved to be more effective with a 5.5% relative prediction error. Besides, some simple data preprocessing methods are tested to make the predictive model simpler and more robust.
加权回归在法国软质小麦产量预测中的应用
准确预测个别作物的产量水平一直是作物部门和政府决策者的重要课题。从全球市场的角度来看,需要这些统计数据来做出准确的价格预测,从而做出商业决策。随着计算机科学和数学的发展以及对开放农业数据集的更容易访问,统计学习方法可以作为这一目的的替代方法。本文将加权统计学习方法应用于1995-2010年法国软质小麦产量预测,并结合相关的方法记录。在预测误差方面,加权回归方法的相对预测误差为5.5%。此外,还测试了一些简单的数据预处理方法,使预测模型更简单、更鲁棒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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