基于时间序列和改进随机森林算法的混凝土价格趋势预测  

Forecast of Concrete Price Movement Based on TimeSeries and Improved Random Forest Model

作　　者：刘庆 黄明浩 LEE Woon-Seek LIU Qing;HUANG Minghao;LEE Woon-Seek(School of Economics and Management,Huainan Normal University,Huainan 232038,China;Graduate School of Management of Technology,Pukyong National University,Busan 48547,Korea)

机构地区：[1]淮南师范学院经济与管理学院,安徽淮南232038 [2]国立釜庆大学技术经营专门大学院,韩国釜山48547

出　　处：《运筹与管理》2024年第6期132-138,共7页Operations Research and Management Science

摘　　要：有效而准确的预测商品混凝土价格变动趋势,对各类建筑的施工规划具有重要意义。相比其他预测模型,随机森林模型具有更高的预测精度。然而不同的数据结构都有其独特之处,针对特定数据结构进行模型优化,有助于提高算法在特定数据上的处理性能。我们针对时间序列分类(TSC:Time Series Classification)的特征提出一种改进随机森林算法。首先将随机森林创建训练子集时的随机抽样调整为倾斜抽样,然后将决策树分裂时的随机特征向量抽样调整为分层抽样,最后以加权投票取代平均投票。实证结果表明相比原始随机森林算法,改进模型具有明显优势,对商品混凝土价格变动的预测准确率达98.4%,预测精度、召回率和F1评分分别为:98.7%,98.2%,98.4%,可以实现了商品混凝土价格变动趋势的精准预测。Ready-mixed concrete is one of the primary materials used in various types of construction,including railways,highways,bridges,tunnels,and buildings.Effectively and accurately predicting the price fluctuation trends of ready-mixed concrete can optimize construction planning,enhance economic benefits for construction enterprises,and hold significant importance for the planning of various construction projects.There are two feasible approaches for modeling the prediction of concrete prices:multivariable modeling and univariable modeling.Multivariable modeling involves first analyzing the factors that influence concrete price fluctuations and establishing related multivariate panel data.In contrast,univariable modeling uses historical price data to predict future prices.This method has the advantages of simple data collection and ease of operation,making it widely used in the prediction of various commodity prices.Existing research indicates that the random forest model exhibits higher predictive accuracy than other forecasting models.However,different data structures have their own unique characteristics.Optimizing the model for specific data structures can help enhance the algorithm’s performance on particular datasets.This paper constructs an autoregressive sequence using concrete price data,transforming the price trend prediction problem into a time series classification(TSC)problem.We then perform logical optimizations on the three core steps of building a random forest model.These enhancements improve the applicability of the random forest model to time series data,thereby increasing its performance in predicting concrete price fluctuation trends.Specifically,we first adjust the random sampling used for creating training subsets in the random forest to skewed sampling,strengthening the association between classification categories and classifiers within the random forest.Next,we modify the random feature vector sampling during decision tree splitting to stratified sampling,which helps preserve the temporal c

关 键 词：价格趋势预测 时间序列分类 优化 混凝土 

分 类 号：F221[经济管理—国民经济] F292