Time Series Representation

Time series representation focuses on transforming raw temporal data into informative, computationally efficient formats suitable for various machine learning tasks like forecasting, classification, and anomaly detection. Current research emphasizes self-supervised learning techniques, often employing transformer-based architectures or diffusion models, to learn robust representations from potentially noisy, incomplete, or high-dimensional data, sometimes incorporating multimodal information. These advancements improve accuracy and efficiency across diverse applications, ranging from healthcare (e.g., predicting disease onset from EHR data) to finance (e.g., portfolio optimization) and beyond, by enabling better feature extraction and model generalization.