Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913923565879296 |
|---|---|
| author | Nelson-Archer, Adam Sen, Aleia Hasani, Meena Al Davila, Sofia Le, Jessica Abbouchi, Omar |
| author_facet | Nelson-Archer, Adam Sen, Aleia Hasani, Meena Al Davila, Sofia Le, Jessica Abbouchi, Omar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach Nelson-Archer, Adam Sen, Aleia Hasani, Meena Al Davila, Sofia Le, Jessica Abbouchi, Omar Statistical Finance Artificial Intelligence Machine Learning I.2.6; I.5.1 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. |
| title | Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach |
| topic | Statistical Finance Artificial Intelligence Machine Learning I.2.6; I.5.1 |
| url | https://arxiv.org/abs/2507.01979 |