Multivariate Approach

Multivariate approaches in data analysis aim to simultaneously model multiple variables, capturing their interdependencies to improve prediction accuracy and enhance interpretability compared to univariate methods. Current research focuses on developing and applying advanced models like neural networks (including LSTMs, GRUs, and autoencoders), spiking neural networks, and tree ensembles to handle multivariate data, particularly in time-series contexts with irregular sampling or missing values. These advancements are significantly impacting fields like healthcare, finance, and environmental science by enabling more accurate predictions, improved anomaly detection, and deeper insights from complex datasets.