用深度学习预测美国职位空缺，效果优于传统方法
Forecasting Labor Demand: Predicting JOLT Job Openings using Deep Learning Model
- 直接输入多源经济指标，用LSTM模型预测未来职位空缺
- 相比ARIMA等传统模型，预测误差更低，趋势捕捉更准
- 适合关注劳动力市场政策与数据驱动决策的机构
本论文研究长短期记忆网络（LSTM）在预测美国职位空缺和劳动力流动调查数据方面的有效性。基于多个来源的经济指标，将原始数据输入LSTM模型以预测未来周期的JOLT职位空缺。模型性能与传统自回归方法（包括ARIMA、SARIMA和Holt-Winters）进行比较。结果表明，LSTM模型在预测JOLT职位空缺方面表现更优，不仅能捕捉因变量的趋势，还能融合关键经济因素。这凸显了深度学习技术在捕捉经济数据复杂时间依赖性方面的潜力，为政策制定者和利益相关方制定数据驱动的劳动力市场策略提供了重要参考。
原文摘要 · Abstract (English)
This thesis studies the effectiveness of Long Short Term Memory model in forecasting future Job Openings and Labor Turnover Survey data in the United States. Drawing on multiple economic indicators from various sources, the data are fed directly into LSTM model to predict JOLT job openings in subsequent periods. The performance of the LSTM model is compared with conventional autoregressive approaches, including ARIMA, SARIMA, and Holt-Winters. Findings suggest that the LSTM model outperforms these traditional models in predicting JOLT job openings, as it not only captures the dependent variables trends but also harmonized with key economic factors. These results highlight the potential of deep learning techniques in capturing complex temporal dependencies in economic data, offering valuable insights for policymakers and stakeholders in developing data-driven labor market strategies
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