The field of time series analysis and clustering is moving towards more innovative and effective methods for handling complex data, such as mixed data and non-tabular data like time series. Researchers are proposing new algorithms and techniques that can effectively represent uncertainty and/or imprecision, and can handle large amounts of data. One of the key trends in this area is the use of structure-aware metrics and canonical correlation patterns to validate clustering of multivariate time series. Another significant direction is the integration of generative artificial intelligence and copula-based modeling to deliver accurate predictions and enable robust anomaly detection. These advancements have the potential to improve the quality and reliability of time series analysis and clustering in various fields, including finance, health, and industrial applications. Noteworthy papers in this area include: Soft-ECM, which proposes a new algorithm for clustering complex data using a semi-metric, and Kolmogorov-Arnold Networks-based GRU and LSTM, which introduces innovative architectures for loan default early prediction. Additionally, SDSC proposes a structure-aware metric function for time series self-supervised representation learning, and CoCAI presents a novel framework for conformal anomaly identification in multivariate time-series.