Multivariate Time Series Data

Multivariate time series data analysis focuses on understanding and extracting insights from data involving multiple variables measured over time, aiming to improve prediction, anomaly detection, and classification accuracy. Current research emphasizes advanced preprocessing techniques to handle missing data and class imbalance, alongside the application of deep learning models like recurrent neural networks (RNNs), transformers, variational autoencoders (VAEs), and graph neural networks (GNNs), often integrated with contrastive learning or generative adversarial networks (GANs). These advancements are crucial for diverse applications, including space weather forecasting, industrial process monitoring, and financial modeling, where accurate predictions and timely anomaly detection are critical for risk mitigation and improved decision-making.