This survey provides a comprehensive and up-to-date overview of Foundation Models (FMs) for time series analysis, focusing on their methodologies and applications. The authors introduce a novel taxonomy that categorizes FMs based on model architecture, pre-training techniques, adaptation methods, and data modalities. The survey highlights the advancements in FMs for various types of time series data, including standard time series, spatial time series, trajectories, and events. It discusses the challenges and opportunities in time series analysis and explores the potential of FMs in these domains. The survey also reviews existing methods for pre-training, adaptation, and modality handling, emphasizing the importance of understanding the underlying mechanisms to enhance the performance of FMs in time series analysis. The authors conclude by discussing future research directions and potential avenues for further exploration in this field.This survey provides a comprehensive and up-to-date overview of Foundation Models (FMs) for time series analysis, focusing on their methodologies and applications. The authors introduce a novel taxonomy that categorizes FMs based on model architecture, pre-training techniques, adaptation methods, and data modalities. The survey highlights the advancements in FMs for various types of time series data, including standard time series, spatial time series, trajectories, and events. It discusses the challenges and opportunities in time series analysis and explores the potential of FMs in these domains. The survey also reviews existing methods for pre-training, adaptation, and modality handling, emphasizing the importance of understanding the underlying mechanisms to enhance the performance of FMs in time series analysis. The authors conclude by discussing future research directions and potential avenues for further exploration in this field.