The field of time series analysis and operator learning is witnessing significant developments, with a focus on improving interpretability, uncertainty quantification, and scalability. Researchers are exploring new approaches to model complex time series data, such as introducing continuous relaxation of discrete states and using generative hyper-networks to estimate predictive uncertainty. Additionally, there is a growing interest in operator learning, which has the potential to drastically reduce expensive numerical integration of PDEs. Noteworthy papers in this area include the introduction of the Gumbel Dynamical Model, which enables fast and scalable training with standard gradient descent methods, and the development of GenUQ, a measure-theoretic approach to uncertainty quantification that avoids constructing a likelihood. Furthermore, the application of Bayesian Transformer models to Pan-Arctic sea ice concentration mapping and uncertainty estimation is showing promising results. Overall, these advancements are expected to have a significant impact on various fields, including climate modeling, finance, and engineering.