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A synergistic enhancement to demand forecasting using neural networks with voids - streamlining model efficiency through optimizing feature selection
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This paper presents a collaborative effort encompassing four key individual contributions:
optimising feature selection in Temporal Fusion Transformers, enhancing anomaly de tection during special events, examining the impact of Cross-Client data integration on
forecasting accuracy, and leveraging Generative AI for strategic business recommenda tions. Collectively, these studies reveal significant advancements in demand forecasting
and management for e-commerce companies. The results demonstrate improved predic tive accuracy, efficient anomaly handling during critical sales periods, insights into the
benefits and limitations of aggregated data models, and advantages of using generative AI
for recommending business action to mitigate operational risks.
Keywords: Neural Networks, Demand Forecasting, Feature Engineering, Runtime Optimisation, Demand Shaping, GenAI, Prompt Engineering, Machine Learning, Time Series,
Cross-client Data, Temporal Fusion Transforme.
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Palavras-chave
Neural networks Demand forecasting Feature engineering Runtime optimisation