Time series analysis is fundamental to understanding temporal patterns and making predictions across diverse domains such as finance, healthcare, climate science, and intelligent transportation. Traditional deep learning approaches have significantly advanced this field, but these models remain task-specific and often require extensive labeled data. Inspired by the success of Foundation Models (FM), especially large language models, researchers have begun exploring the concept of Time Series Foundation Models to enhance adaptability and generalization across diverse time series tasks. This tutorial aims to provide a comprehensive review of Foundation Models for Time Series, covering theory, algorithms, and applications, categorizing existing methodologies and identifying key research directions.