基于SVR-CV-GS模型的湖北省物流需求预测研究

Research on Logistics Demand Forecast in Hubei Province Based on SVR-CV-GS Model

摘要: 随着城市物流需求的不断增长，精确的物流需求预测对提高资源配置效率、优化物流系统至关重要。本文提出了一种基于交叉验证法和网格搜索法优化支持向量回归模型的物流需求预测方法。首先，我们利用SVR模型进行物流需求的回归分析，并使用交叉验证法选择最优的核函数(包括线性核、径向基核项式核)。接着，为了提高SVR模型的预测精度，本文引入了网格搜索法对SVR的超参数进行优化，包括惩罚因子C、容忍度和核函数的参数。通过对湖北省历史物流需求数据的实验分析，验证了所提出方法在提高预测精度和模型稳定性方面的有效性。实验结果表明，与传统SVR模型相比，采用SVR-CV-GS模型能够显著提升预测准确性，为城市物流需求的科学决策提供有效支持。

Abstract: With the continuous growth of urban logistics demand, accurate logistics demand forecasting is essential to improve the efficiency of resource allocation and optimize the logistics system. In this paper, we propose a logistics demand forecasting method based on the cross-validation method and the grid search method to optimize the Support Vector Regression (SVR) model. Firstly, we use the SVR model to perform regression analysis of logistics demand, and use the cross-validation method to select the optimal kernel function (including linear kernel and radial basis kernel). Then, in order to improve the prediction accuracy of the SVR model, this paper introduces the grid search method to optimize the hyperparameters of the SVR, including the parameters of penalty factor C, tolerance and kernel function. Through the experimental analysis of the historical logistics demand data of Hubei Province, the effectiveness of the proposed method in improving the prediction accuracy and model stability is verified. Experimental results show that compared with the traditional SVR model, the SVR-CV-GS model can significantly improve the prediction accuracy and provide effective support for the scientific decision-making of urban logistics demand.