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Dynamic pricing and demand forecasting: Integrating time-series analysis, regression models, machine learning, and competitive analysis
- 1 Beijing University of Posts and Telecommunications, Beijing, China
- 2 University College London, London, United Kingdom of Britain
- 3 University of New South Wales, Sydney, Australia
* Author to whom correspondence should be addressed.
Abstract
Dynamic pricing is a critical strategy for businesses seeking to optimize revenue and stay competitive in fluctuating markets. This paper explores the integration of various demand forecasting techniques, including time-series analysis, regression models, and machine learning algorithms, with competitive analysis methodologies to enhance dynamic pricing strategies. Time-series analysis focuses on decomposing data into trend, seasonality, and random fluctuations, using ARIMA models for accurate demand prediction. Regression models delve into the complexities of variable interactions, extending beyond linear relationships to include advanced techniques like Ridge, Lasso, and Elastic Net regression. Machine learning algorithms, such as decision trees, random forests, gradient boosting, and neural networks, revolutionize demand forecasting by uncovering complex patterns in large datasets. Competitive analysis incorporates market scanning, price elasticity estimation, and competitor behavior modeling to inform dynamic pricing decisions. Optimization algorithms, including linear programming, genetic algorithms, and simulated annealing, are employed to refine pricing strategies, while revenue management techniques like yield management and overbooking ensure maximum revenue from perishable goods and services. This comprehensive approach enables businesses to dynamically adjust prices in real-time, maintaining competitiveness and maximizing profitability in an ever-evolving market landscape.
Keywords
Dynamic pricing, demand forecasting, time-series analysis, ARIMA
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Cite this article
Ma,Q.;Feng,S.;Liu,J. (2024). Dynamic pricing and demand forecasting: Integrating time-series analysis, regression models, machine learning, and competitive analysis. Applied and Computational Engineering,93,149-154.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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