标题： 用LSTNet预测劳动力市场：一种多尺度深度学习方法 显示英文标题

标题： Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach

摘要： 我们提出了一种深度学习方法，利用美国劳工统计局的劳动力市场数据来预测短期就业变化并评估长期行业健康状况。 我们的系统采用长短期时间序列网络（LSTNet）来处理多变量时间序列数据，包括就业水平、工资、人员流动率和职位空缺。 该模型同时输出7天的就业预测和一个可解释的行业就业健康指数（IEHI）。 我们的方法在大多数行业中优于基线模型，特别是在稳定行业中，IEHI排名与实际就业波动性表现出很强的一致性。 我们讨论了错误模式、行业特定表现以及提高可解释性和泛化能力的未来方向。 显示英文摘要

摘要： We present a deep learning approach for forecasting short-term employment changes and assessing long-term industry health using labor market data from the U.S. Bureau of Labor Statistics. Our system leverages a Long- and Short-Term Time-series Network (LSTNet) to process multivariate time series data, including employment levels, wages, turnover rates, and job openings. The model outputs both 7-day employment forecasts and an interpretable Industry Employment Health Index (IEHI). Our approach outperforms baseline models across most sectors, particularly in stable industries, and demonstrates strong alignment between IEHI rankings and actual employment volatility. We discuss error patterns, sector-specific performance, and future directions for improving interpretability and generalization.