  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
https://github.com/Job-SDF/benchmark.
