Comparative Analysis of Time Series Forecasting Models for Predicting Hydrogen Fuel-Related Stock in the Indian Market
インド市場における水素燃料関連株式の予測のための時系列予測モデルの比較分析 (AI 翻訳)
Angel Mary Jais, Nandhini Haribabu Muthuvel, Sunanda Saha, Abhishek Das, Venkatesh Subramanian
🤖 gxceed AI 要約
日本語
インドの水素関連企業（L&T、NTPC、JSW Energy、Adani Green）の株価予測にARIMA、LSTM、CatBoost、XGBoost、LightGBMを比較。LSTMが短期予測で最良、LightGBMが安定した性能を示した。機械学習の再生可能エネルギー株式市場への応用の重要性を強調。
English
This study compares time series and ML models (ARIMA, LSTM, CatBoost, XGBoost, LightGBM) for predicting stock movements of Indian hydrogen-related companies. LSTM showed best short-term accuracy, LightGBM provided robust performance. Highlights ML's growing role in renewable energy equity markets.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でも水素関連銘柄への関心が高まっているが、本論文はインド市場の事例であり、直接の示唆は限定的。ただし、機械学習を用いた株式予測手法は日本市場にも応用可能。
In the global GX context
This paper applies ML forecasting to hydrogen-related stocks in India, a growing GX market. Globally, it demonstrates how time series models can be used for green equity analysis, relevant for investors in transition finance.
👥 読者別の含意
🔬研究者:Provides comparative performance of ML models for hydrogen stock prediction in India.
🏢実務担当者:Useful for investors tracking hydrogen stocks using ML.
🏛政策担当者:Offers data-driven insights into hydrogen equity market dynamics.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.33889/ijmems.2026.11.3.058first seen 2026-06-10 05:31:45
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