Angel Mary Jais
School of Chemical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Nandhini Haribabu Muthuvel
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Sunanda Saha
Centre for Clean Environment, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Abhishek Das
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Venkatesh Subramanian
Department of Biotechnology, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India.

DOI https://doi.org/10.33889/IJMEMS.2026.11.3.058

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Abstract

The study compares the capabilities of various time series and machine learning models including ARIMA, LSTM, CatBoost, XGBoost, and LightGBM, by predicting the equity movement for major Indian infrastructure and energy companies with hydrogen related exposure, namely Larsen & Toubro, NTPC Limited, JSW Energy Limited, and Adani Green Energy Limited. Hydrogen fuel is considered the most promising energy provider of the future, and an understanding of its position in the market is vital for its growth. The study uses historical data involving stock prices from April 2019 through April 2024 obtained from the National Stock Exchange of India. Using open price as the primary variable, the performance of the models is measured. Additional variables such as close price, highest price, lowest price, and volume are used for gradient boosting. Output graphs comparing actual prices and predicted prices are obtained. The results indicate that deep learning and gradient boosting outperform the statistical model. LSTM demonstrated the strongest short-term predictive accuracy through sequential learning among all models. Among the gradient boosting models, LightGBM provides consistent and robust performance by effectively capturing nonlinear feature interactions. Overall, the study highlights the growing importance of machine learning in interpreting India’s renewable energy equity markets.

Keywords- Time series, LSTM, Gradient boosting, Stock market forecasting, Hydrogen fuel-related stock.

Citation

Jais, A. M. Muthuvel, N. H. Saha, S., Das, A., & Subramanian, V. (2026). Comparative Analysis of Time Series Forecasting Models for Predicting Hydrogen Fuel-Related Stock in the Indian Market. International Journal of Mathematical, Engineering and Management Sciences, 11(3), 1424-1443. https://doi.org/10.33889/IJMEMS.2026.11.3.058.