用深度学习预测就业变化，同时生成可解释的行业健康指数。
Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach
- 基于LSTNet处理多变量时间序列数据，融合就业、工资等指标。
- 在多数行业中优于基线模型，尤其在稳定行业表现突出。
- 输出7天就业预测与可解释的行业健康指数，适合政策制定者参考。
我们提出一种深度学习方法，用于预测美国劳工统计局提供的劳动力市场数据中的短期就业变动，并评估长期行业健康状况。系统采用长短期时间序列网络（LSTNet）处理多变量时间序列数据，包括就业水平、工资、离职率和职位空缺。模型输出7天就业预测结果以及可解释的行业就业健康指数（IEHI）。实验表明，该方法在多数行业中优于基线模型，尤其在稳定行业中表现更佳，且IEHI排名与实际就业波动高度一致。我们分析了误差模式、行业特异性表现，并探讨了提升可解释性与泛化能力的未来方向。
原文摘要 · Abstract (English)
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.
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