Recent development in deep learning has spurred research advances in time series modeling and analysis. Practical applications of time series raise a series of new challenges in science applications, such as multi-resolution, multimodal, missing value, distributeness, and interpretability. By bridging deep learning advances with large language models, I will highlight the next frontier in time series research, i.e., developing foundation models that enhance forecasting performance, generative models, and reasoning capabilities from time series across diverse applications.