Why Use K-Means for Time Series Data? (Part One)

K-Means clustering is an unsupervised learning technique used for organizing data points into groups based on their similarity, maximizing data similarity within clusters and minimizing it across clusters. It's particularly useful for time series data analysis as it can help detect anomalies such as one-off spikes, tightly packed data with controlled systems, or normal distributions. The technique can also be applied to contextual anomaly detection, where the system is trained to recognize patterns in healthy, normal signals, allowing it to predict and reconstruct new data points and measure error to determine if an anomaly is present.