{"id":10235,"date":"2026-08-19T21:39:07","date_gmt":"2026-08-19T13:39:07","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=10235"},"modified":"2026-08-21T15:11:56","modified_gmt":"2026-08-21T07:11:56","slug":"jase-202611-34-054","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-054","title":{"rendered":"AI-Driven Labor Market Forecasting: Predicting Sectoral Shifts and Demand for Future Skills"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Juan Weng<\/i><\/a> <\/p>\n\n\n\n

Xiangtan Institute of Technology, Xiangtan, China<\/p>\n<\/div>\n\n\n\n

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Received: May 04, 2026
Accepted: June 19, 2026
Publication Date: August 19, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Quantitative Evaluation of Forecasting Performance Metrics <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.054<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Recent advances in artificial intelligence (AI) and deep learning (DL) have significantly improved the ability to analyze and forecast economic and labor market trends. However, accurately predicting sector-specific employment remains challenging due to nonlinear relationships among macroeconomic variables and long term temporal dependencies that traditional econometric and recurrent models often fail to capture. To address these limitations, this study proposes a multivariate deep learning approach based on the Patch Time Series Transformer (PatchTST) architecture. The model is designed to forecast labor market dynamics, identify sectoral employment trends, and analyze evolving patterns of future skill demand. The proposed framework utilizes structured data from the International Labour Organization Statistics (ILOSTAT), incorporating key indicators such as employment, unemployment, inflation, and productivity. Data preprocessing techniques, including missing value imputation, temporal alignment, outlier treatment, and z-score normalization, are applied to ensure data quality and consistency. The time series data are further transformed into modelcompatible inputs using sliding-window sequences and patch-based segmentation, enabling efficient learning within the Transformer architecture. PatchTST leverages multi-head selfattention mechanisms to capture complex temporal patterns and inter-sectoral relationships, allowing for accurate multi-horizon forecasting. Experimental results demonstrate strong predictive performance, with a Mean Absolute Error (MAE) of 0.0103 , Root Mean Squared Error (RMSE) of 0.0161 , Mean Squared Error (MSE) of 0.0003 , and an R2<\/sup> value of 0.9906 , indicating a close fit between predicted and actual values. Overall, the findings suggest that the proposed model provides a robust, scalable, and data-driven solution for workforce planning and future skill demand analysis.<\/p>\n\n\n\n

Keywords: AI, Labor market forecasting; Sectoral employment forecasting; Future skill demand; DL forecasting; Multivariate time series prediction.<\/em><\/p>\n\n\n\n

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