Semi-supervised time series classification

Semi-supervised time series classification is a method of time series analysis that combines a small amount of labeled data with a large amount of unlabeled data, aiming to improve the accuracy and generalization ability of classification models by leveraging the potential structural information in the unlabeled data. This approach is particularly valuable in scenarios where the cost of data annotation is high or difficult to obtain, and it is widely applied in fields such as financial forecasting, medical diagnosis, and industrial monitoring.