The field of time series analysis and modeling is moving towards more robust and generalizable solutions, with a focus on learning from irregular and sparse data. Researchers are exploring new approaches to modeling continuous-time dynamics, such as using invertible neural flows and discretization schemes that preserve flatness. Additionally, there is a growing interest in time series foundation models and their applications in various domains, including hydrology and bearing health status classification. These models are being improved with techniques such as in-context learning and frequency-domain formulation, enabling more efficient and accurate forecasting. Noteworthy papers include FlowPath, which learns the geometry of the control path via an invertible neural flow, and FreqFlow, which leverages conditional flow matching in the frequency domain for deterministic multivariate time-series forecasting. Overall, the field is advancing towards more powerful and flexible models that can handle complex and heterogeneous time series data.