2025

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Browsing 2025 by Author "Safi Ullah Zahid"

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    GRAPH NEURAL NETWORK GNN FOR RETAIL FORECASTING: A TEMPORAL ENCODING APPROACH FOR SALES FORECASTING
    (2025) Safi Ullah Zahid
    Effective sales forecasting is a key factor in retail decision-making, allowing businesses to maximize inventory, improve supply chains, and provide superior customer experience. Traditional approaches using statistical models or machine learning fail to capture complex interdependencies among products, customers, and trends over time. In this research, we present a new forecasting framework that enriches temporal encoding using Graph Neural Networks (GNNs) to model and forecast retail sales. Leveraging Kaggle open-source sales data, the suggested model forms dynamic graphs of temporal interactions and item interactions, enabling the GNN to comprehend changing relationships over time. Experimental results showcase significant advantages when applying a GNN-based model compared to traditional methods like SARIMA and state-of-the-art deep learning architectures such as Long Short-Term Memory (LSTM) networks, achieving a markedly better prediction accuracy across various temporal horizons, especially in dynamic retail settings. Starting from the graph elaboration (the incorporation of temporal encoding) to the optimization using attention mechanisms and finally the evaluation in terms of key performance metrics (RMSE and MAE) in which evidence is given of the relevance of the approach. This method yields enhanced scalability and flexibility, especially for real-time retail forecasting use cases. The results underscore the potential of GNNs to improve forecasting accuracy, leading to better-informed business decisions and advancing the emerging area of temporal graph applications in business analytics