Enhancing Natural Gas Price Prediction: A Machine Learning and Explainable AI Approach
- Title
- Enhancing Natural Gas Price Prediction: A Machine Learning and Explainable AI Approach
- Creator
- Singh K.; Mahajan J.; Singh V.P.; Paswan A.S.; Saxena A.
- Description
- This research includes an innovative approach to refine natural gas price predictions by employing advanced machine learning techniques, including Random Forest, Linear Regression, and Support Vector Machine algorithms. Against the backdrop of natural gas's increasing influence in the energy sector, both environmentally and economically, the study adopts a robust methodology using a comprehensive dataset from Kaggle. Through rigorous data preprocessing, feature engineering, and model training, the chosen algorithms are optimized to capture complex patterns within the data, demonstrating the potential to significantly enhance forecast precision. The application of these techniques aims to extract meaningful insights, providing stakeholders in the natural gas market with more accurate and reliable predictions, there by contributing to a deeper understanding of market dynamics and informed decision- making. 2024 IEEE.
- Source
- TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Feature Engineering; Machine Learning Algorithms; Model Evaluation; Natural Gas; Predictive Analysis
- Coverage
- Singh K., Christ University, India; Mahajan J., Christ University, India; Singh V.P., Ewec, United Arab Emirates; Paswan A.S., Christ University, India; Saxena A., Christ University, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038427-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Singh K.; Mahajan J.; Singh V.P.; Paswan A.S.; Saxena A., “Enhancing Natural Gas Price Prediction: A Machine Learning and Explainable AI Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19176.