Abstract— The ever-changing nature of the information technology job market is a challenge for students and professionals to predict what kind of job opportunities will arise in the near future. Conventional job search sites are limited to presenting existing job openings. In this context, we propose a novel intelligent analytics framework for predicting future job demand in the IT field. The framework is named HireCast Engine. It uses APIs from Adzuna and JSearch to fetch job postings and form a structured time series dataset based on job demand in different roles and cities. The framework uses a hybrid Prophet-LSTM model to predict future job demand. In addition to forecasting, a graph-based skill gap analysis module is implemented using the Neo4j graph database to analyze skill gaps and recommend appropriate career paths. Experimental results on 31,200 job postings from 12 cities in India proved that the hybrid Prophet-LSTM model is more accurate in predicting job demand, achieving 96.7% accuracy. The proposed framework provides an effective decision-support system for career planning and job market analytics.
Y et al. (2026) studied this question.