In a rapidly evolving job market, skill demand forecasting is crucial as it
enables policymakers and businesses to anticipate and adapt to changes,
ensuring that workforce skills align with market needs, thereby enhancing
productivity and competitiveness. Additionally, by identifying emerging skill
requirements, it directs individuals towards relevant training and education
opportunities, promoting continuous self-learning and development. However, the
absence of comprehensive datasets presents a significant challenge, impeding
research and the advancement of this field. To bridge this gap, we present
Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting
models. Based on 10.35 million public job advertisements collected from major
online recruitment platforms in China between 2021 and 2023, this dataset
encompasses monthly recruitment demand for 2,324 types of skills across 521
companies. Our dataset uniquely enables evaluating skill demand forecasting
models at various granularities, including occupation, company, and regional
levels. We benchmark a range of models on this dataset, evaluating their
performance in standard scenarios, in predictions focused on lower value
ranges, and in the presence of structural breaks, providing new insights for
further research. Our code and dataset are publicly accessible via the
this https URL

在迅速发展的就业市场中，技能需求预测对于政策制定者和企业来说至关重要，因为它使其能够预测和适应变化，确保劳动力技能与市场需求相吻合，从而提高生产力和竞争力。然而，缺乏全面的数据集对于研究和这一领域的进展存在重大挑战。为了填补这一差距，我们提出了 Job-SDF，这是一个设计用来训练和基准测试职业技能需求预测模型的数据集。我们的数据集在不同的粒度上评估技能需求预测模型的性能，包括职业、公司和地区水平。我们在此数据集上对一系列模型进行基准测试，在标准情况下、低值范围的预测和结构性突变的情况下评估它们的性能，为进一步的研究提供了新的见解。