ISSN 2581-5369
HeinOnline, MANUPATRA, Google Scholar Indexed
Beyond Forecasting: Predictive Quarterly Intelligence Strategic Edge
-
Jhanavarshini K.L.
Graduate from Department of Electronics and Communication, Anna University, Chennai, Tamil Nadu, India
Abstract
This research presents a data-driven framework for predicting quarterly EV demand, enabling automakers to navigate the transition from internal combustion engines to electric vehicles. By integrating multi-source analytics—including historical sales, stock price correlations, macroeconomic indicators, and charging infrastructure growth—we develop high-accuracy forecasting models that reduce prediction errors by 20% compared to traditional methods. The system incorporates real-time monitoring as of April 2025 to dynamically adjust for variables like lithium price swings and subsidy changes. A dual-layer governance mechanism ensures both analytical integrity and legal compliance ,Technical Governance and Regulatory Alignment.
Keywords
- Electric Vehicle
- Business Research
- Antitrust Compliance
- Legal AI Governance
Type
Research Paper
Information
International Journal of Law Management and Humanities, Volume 8, Issue 2, Page 4479 - 4485
DOI: https://doij.org/10.10000/IJLMH.119528Creative Commons
This is an Open Access article, distributed under the terms of the Creative Commons Attribution -NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting, and building upon the work for non-commercial use, provided the original work is properly cited.
Copyright
Copyright © IJLMH 2021