The field of time series analysis is moving towards more robust and efficient evaluation metrics, with a focus on handling irregularly sampled data and incorporating temporal dependencies. Researchers are exploring novel approaches to time series anomaly detection, including the use of Bayesian estimation, time embeddings, and periodicity-aware latent-state representation learning. These advancements aim to improve the accuracy and reliability of time series analysis in various applications, such as stress monitoring and healthcare. Noteworthy papers include: CCE, which introduces a novel evaluation metric that measures prediction confidence and uncertainty consistency, and PLanTS, which proposes a periodicity-aware self-supervised learning framework for multivariate time series. These innovative approaches are expected to advance the field of time series analysis and have significant implications for real-world applications.