A Multi-Temporal Approach to Dynamic Price Optimization: Integrating Machine Learning with Seasonal Decomposition for Real-Time Demand Forecasting

Author(s)	Anirudh Reddy Pathe
Country	United States
Abstract	Dynamic price optimization enhances revenue and customer satisfaction in e-commerce by leveraging real-time demand and market trends. Integrating machine learning with seasonal decomposition enables accurate multi-temporal demand forecasting by isolating trends, seasonality, and anomalies. This paper reviews existing approaches, highlights benefits such as improved accuracy and interpretability, and addresses challenges like data quality and real-time implementation. Future directions include advancements in deep learning, broader applications, and real-time optimization through IoT.
Keywords	Dynamic pricing, machine learning, seasonal decomposition, time-series forecasting
Field	Engineering
Published In	Volume 2, Issue 1, January-February 2020
Published On	2020-01-08
DOI	https://doi.org/10.36948/ijfmr.2020.v02i01.22333
Short DOI	https://doi.org/hbbthw

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International Journal For Multidisciplinary Research