Multivariate Model

Multivariate models analyze datasets with multiple interacting variables, aiming to uncover relationships and make accurate predictions exceeding the capabilities of univariate approaches. Current research focuses on improving model efficiency and interpretability, exploring architectures like Transformers and graph neural networks, and addressing challenges like uncertainty quantification and handling sparse or missing data. These advancements are crucial for diverse applications, including time series forecasting, classification with uncertainty awareness, and virtual sensing, ultimately leading to more robust and insightful analyses across various scientific domains.