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This Ph.D. research proposal focuses on developing specialized Transformer architectures for time series modeling and forecasting, addressing the limitations of traditional methods. The research aims to enhance forecasting accuracy, scalability, and interpretability by creating models that account for the unique characteristics of time series data, such as temporal structure and irregular sampling. The study will be aligned with the Lusis - CentraleSupélec research chair, benefiting applications in credit c
- Author
- gelanelemowayu
- Language
- EN