Research on Human Resource Demand Forecasting Based on PSO-SVM Algorithm

Accurate demand forecasting in human resource management is crucial for optimizing resource allocation and improving organizational efficiency. This study proposes a hybrid algorithm combining Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) for human resource demand forecasting. The PSO algorithm is utilized to optimize the parameters of the SVM model, enhancing its prediction accuracy and generalization capability. Key influencing factors of human resource demand are identified and processed through feature engineering. Subsequently, a PSO-SVM prediction model is developed, where PSO optimizes the kernel function parameters and penalty factors of SVM, addressing the limitations of traditional SVM in parameter selection and local optima. An empirical study based on historical data from an enterprise demonstrates the superiority of the PSO-SVM model in terms of prediction accuracy and computational efficiency. Experimental results show that the PSO-SVM algorithm outperforms conventional SVM and other optimization methods, such as grid search, in evaluation metrics like Mean Squared Error (MSE) and Mean Absolute Error (MAE). This study provides an efficient and reliable solution for human resource demand forecasting, with potential applications in other domains.