摘 要：随着经济全球化和市场竞争加剧，企业对人力资源管理的科学性和前瞻性要求日益提高，而人力资源需求预测作为规划的核心环节，直接影响企业的战略实施与资源配置效率。本研究以企业人力资源需求预测为切入点，旨在探讨适用于不同场景的预测方法及其实际应用效果。通过文献分析、案例研究和实证检验相结合的方式，系统梳理了定性与定量预测方法的特点及适用范围，并提出基于数据驱动的混合预测模型，有效弥补单一方法在复杂环境下的局限性。研究结果表明，该模型能够显著提升预测精度，为企业制定精准的人力资源规划提供决策支持。本研究的创新点在于将机器学习算法融入传统预测框架，同时结合行业特征优化模型参数，从而增强其普适性和实用性。这一成果不仅丰富了人力资源管理理论，也为企业在动态环境中实现高效资源配置提供了重要参考。
关键词：人力资源需求预测；混合预测模型；机器学习算法
Abstract：With the deepening of economic globalization and intensifying market competition, enterprises are placing increasingly higher demands on the scientific rigor and foresight of human resource management. As a core component of planning, human resource demand forecasting directly impacts the implementation of corporate strategies and the efficiency of resource allocation. This study focuses on enterprise human resource demand forecasting, aiming to explore predictive methods suitable for different scenarios and their practical application effects. By integrating literature analysis, case studies, and empirical testing, this research systematically examines the characteristics and applicability of both qualitative and quantitative forecasting methods, proposing a data-driven hybrid prediction model that effectively addresses the limitations of single-method approaches in complex environments. The results indicate that this model significantly enhances forecasting accuracy, providing decision support for enterprises to develop precise human resource plans. The innovation of this study lies in incorporating machine learning algorithms into traditional forecasting fr ameworks while optimizing model parameters based on industry-specific features, thereby enhancing its generalizability and practicality. This achievement not only enriches the theoretical foundation of human resource management but also offers critical insights for enterprises to achieve efficient resource allocation in dynamic environments.
引言 1
一、人力资源需求预测的理论基础 1
(一)需求预测的基本概念 1
(二)预测方法的分类与特点 2
(三)理论框架在实践中的应用 2
二、定性预测方法及其应用分析 2
(一)德尔菲法的应用场景 3
(二)主观判断法的优势与局限 3
(三)定性方法的优化策略 4
三、定量预测方法的研究与实践 4
(一)回归分析法的操作步骤 4
(二)时间序列法的适用范围 4
(三)数据驱动的定量预测改进 5
四、需求预测方法的综合应用与案例研究 5
(一)混合预测模型的设计思路 5
(二)实际案例中的方法选择与实施 6
(三)预测效果评估与改进建议 6
结论 7
参考文献 8
致谢 8
关键词：人力资源需求预测；混合预测模型；机器学习算法
Abstract：With the deepening of economic globalization and intensifying market competition, enterprises are placing increasingly higher demands on the scientific rigor and foresight of human resource management. As a core component of planning, human resource demand forecasting directly impacts the implementation of corporate strategies and the efficiency of resource allocation. This study focuses on enterprise human resource demand forecasting, aiming to explore predictive methods suitable for different scenarios and their practical application effects. By integrating literature analysis, case studies, and empirical testing, this research systematically examines the characteristics and applicability of both qualitative and quantitative forecasting methods, proposing a data-driven hybrid prediction model that effectively addresses the limitations of single-method approaches in complex environments. The results indicate that this model significantly enhances forecasting accuracy, providing decision support for enterprises to develop precise human resource plans. The innovation of this study lies in incorporating machine learning algorithms into traditional forecasting fr ameworks while optimizing model parameters based on industry-specific features, thereby enhancing its generalizability and practicality. This achievement not only enriches the theoretical foundation of human resource management but also offers critical insights for enterprises to achieve efficient resource allocation in dynamic environments.
Keywords: Human Resource Demand Forecasting；Hybrid Forecasting Model；Machine Learning Algorithm
引言 1
一、人力资源需求预测的理论基础 1
(一)需求预测的基本概念 1
(二)预测方法的分类与特点 2
(三)理论框架在实践中的应用 2
二、定性预测方法及其应用分析 2
(一)德尔菲法的应用场景 3
(二)主观判断法的优势与局限 3
(三)定性方法的优化策略 4
三、定量预测方法的研究与实践 4
(一)回归分析法的操作步骤 4
(二)时间序列法的适用范围 4
(三)数据驱动的定量预测改进 5
四、需求预测方法的综合应用与案例研究 5
(一)混合预测模型的设计思路 5
(二)实际案例中的方法选择与实施 6
(三)预测效果评估与改进建议 6
结论 7
参考文献 8
致谢 8
