K-Means is an unsupervised learning technique used for clustering, and it can be applied to time series data, which are sequences of measurements taken at regular intervals over time. Anais Dotis-Georgiou explains that K-Means clustering is particularly useful for time series data because it allows for the identification of patterns or groups in the data without requiring any prior knowledge of the underlying structure. The technique can be used to reduce the dimensionality of high-dimensional time series data, making it easier to analyze and visualize. By grouping similar values together, K-Means clustering can help to highlight trends and anomalies in the data, providing valuable insights for time series analysis and forecasting.