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

ArXiv ID: 2507.01979 “View on arXiv”

Authors: 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.

Keywords: deep learning, time-series forecasting, labor market, LSTNet, employment health index

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced deep learning architectures (LSTNet with CNNs, GRUs, and skip connections) and includes formal mathematical definitions for evaluation metrics like SMAPE and RMSE. It demonstrates strong empirical rigor with specific data sources (BLS), preprocessing steps (interpolation, sliding windows), sector-level performance analysis, and clear quantitative results.
  flowchart TD
    Goal["Research Goal: Forecast employment & assess industry health"] --> Data["Data: BLS multivariate time series\n(employment, wages, turnover, openings)"]
    Data --> Method["Methodology: Long- and Short-Term\nTime-series Network LSTNet"]
    Method --> Compute["Computational Process:\n7-day forecast + Industry\nEmployment Health Index IEHI"]
    Compute --> Outcomes["Outcomes: Outperforms baselines\nStrong IEHI alignment with volatility\nSector-specific insights"]