Time series data are pervasive across domains such as finance, healthcare, industrial monitoring, and climate science, where they support forecasting, classification, and anomaly detection tasks. Among these, anomaly detection is especially critical for ensuring reliability, safety, and efficiency, yet it remains challenging due to non-stationarity, noise, high dimensionality, and distribution shifts. Existing approaches include forecasting-based, reconstruction-based, encoding-based, and distance-based methods. Within the distance-based family, the Matrix Profile has become a widely adopted tool thanks to its efficiency, interpretability, and low parameter requirements. Extensions such as MERLIN and DAMP further improve scalability and robustness, particularly in large-scale or streaming settings. At the same time, recent progress in large language models (LLMs) has created new opportunities for time series analysis.