Time Series Clustering

Time series clustering aims to group similar time series data based on their temporal patterns, enabling efficient analysis of large, unlabeled datasets. Current research emphasizes developing robust algorithms that handle complex, high-dimensional data, focusing on ensemble methods, deep learning architectures (like autoencoders and recurrent neural networks), and novel distance metrics tailored to time series characteristics (e.g., dynamic time warping variants). These advancements improve clustering accuracy, interpretability, and efficiency, with applications ranging from financial forecasting and industrial process optimization to healthcare monitoring and customer segmentation.