General Time Series

General time series analysis aims to extract meaningful information and predictions from sequential data across diverse domains. Current research heavily emphasizes the development of general-purpose models, often pre-trained on massive datasets, that can perform various tasks (forecasting, classification, anomaly detection) with minimal task-specific fine-tuning, utilizing architectures like transformers and employing techniques such as contrastive learning and frequency-based feature extraction. These advancements are improving the efficiency and accuracy of time series analysis across numerous fields, from finance and healthcare to engineering and environmental monitoring, by enabling more robust and adaptable solutions.