Advancements in Sales Forecasting: A Critical Evaluation of Machine Learning Techniques and Approaches
Advancements in Sales Forecasting: A Critical Evaluation of Machine Learning Techniques and Approaches |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-8 |
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| Year of Publication : 2025 | ||
| Author : Tanvi kamal Rana, Hina Jignesh Chokshi | ||
| DOI : 10.14445/22315381/IJETT-V73I8P108 | ||
How to Cite?
Tanvi kamal Rana, Hina Jignesh Chokshi,"Advancements in Sales Forecasting: A Critical Evaluation of Machine Learning Techniques and Approaches", International Journal of Engineering Trends and Technology, vol. 73, no. 8, pp.101-111, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I8P108
Abstract
Sales forecasting is crucial in the automotive sector, enabling organizations to optimize inventory, production, and financial planning. Traditional forecasting methods often fail to capture complex sales patterns, leading to inaccurate predictions. Machine learning techniques have suggestively enhanced forecasting accuracy using algorithms that discover hidden patterns. This work inspects key machine learning methods for sales forecasting, especially on models like XGBoost, Random Forest, Support Vector Machines, and deep learning. It discovers the role of feature engineering, data preprocessing, and model selection in enhancing predictive performance. Additionally, the research evaluates these models’ flexibility and mathematical competence, especially concerning big automotive datasets. A relative study highlights their strengths and limits in terms of accuracy, interpretability, and real-world application. Regardless of their advantages, machine learning models face challenges such as overfitting, data scarcity, and dependence on external market issues like economic patterns and consumer behavior. This work confers strategies to progress prediction reliability, including hybrid modeling, ensemble learning, and macroeconomic factor integration. The growth of adaptive models that can expect in real time and make choices automatically should be the main objective of future research. This work thoroughly examines how refined machine learning techniques affect sales forecasting accuracy in the automotive sector.
Keywords
Automotive sector, Machine Learning, Predictive analytics, Sales forecasting, XGBoost.
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