Time series analysis has progressed from traditional autoregressive models to deep learning, Transformers, and foundation models (FMs), including large language models (LLMs) and large vision models (LVMs). These advances have expanded model design possibilities and, notably, enabled time series problem-solving across multiple modalities, greatly enhancing downstream applications in domains such as climate, agriculture, and healthcare. In this talk, I will provide an overview of recent developments in large FMs for time series, highlighting frameworks for transferring knowledge from other modalities to time series, and identifying the advantages of LVMs in cross-modal knowledge transfer over LLMs. I will then delve into our recent research on LVMs for time series, discussing (1) mainstream techniques for imaging time series (i.e., transforming time series to images); (2) empirical findings on LVMs' effectiveness and limitations in time series modeling; and (3) integration of LVMs in multimodal frameworks for time series encoding. Finally, this talk will conclude with an application of LVMs in brain time series learning in neuroscience. The aim of the talk is to review state-of-the-art AI techniques for time series, highlight unique challenges, and share our recent findings in this promising area.