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

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  • Adam Nelson-Archer
  • Aleia Sen
  • Meena Al Hasani
  • Sofia Davila
  • Jessica Le
  • Omar Abbouchi

Abstract

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.

Suggested Citation

  • Adam Nelson-Archer & Aleia Sen & Meena Al Hasani & Sofia Davila & Jessica Le & Omar Abbouchi, 2025. "Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach," Papers 2507.01979, arXiv.org.
    • Handle: RePEc:arx:papers:2507.01979
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    References listed on IDEAS

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    3. Mohit Apte & Yashodhara Haribhakta, 2024. "Advancing Financial Forecasting: A Comparative Analysis of Neural Forecasting Models N-HiTS and N-BEATS," Papers 2409.00480, arXiv.org, revised Sep 2024.
    4. Sebastian Heise & Jeremy Pearce & Jacob P. Weber, 2024. "Wage Growth and Labor Market Tightness," Staff Reports 1128, Federal Reserve Bank of New York.
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    7. Flores, Benito E, 1986. "A pragmatic view of accuracy measurement in forecasting," Omega, Elsevier, vol. 14(2), pages 93-98.
    8. Mustafa Yurtsever, 2023. "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-9, December.
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