The paper introduces the Pruned Exact Linear Time (PELT) method for detecting multiple changepoints in large datasets, focusing on scenarios where the number of changepoints increases as more data is collected. PELT is designed to have a computational cost that is linear in the number of observations, which is a significant improvement over existing methods that can have quadratic or cubic computational costs. The method is based on dynamic programming and involves a pruning step to reduce computational complexity while maintaining exactness. PELT is compared with other methods such as Binary Segmentation and Optimal Partitioning in simulations and real data examples, showing that it is significantly faster and more accurate, particularly for long datasets. The paper also discusses the conditions under which PELT can achieve linear computational complexity and provides theoretical results supporting its efficiency.The paper introduces the Pruned Exact Linear Time (PELT) method for detecting multiple changepoints in large datasets, focusing on scenarios where the number of changepoints increases as more data is collected. PELT is designed to have a computational cost that is linear in the number of observations, which is a significant improvement over existing methods that can have quadratic or cubic computational costs. The method is based on dynamic programming and involves a pruning step to reduce computational complexity while maintaining exactness. PELT is compared with other methods such as Binary Segmentation and Optimal Partitioning in simulations and real data examples, showing that it is significantly faster and more accurate, particularly for long datasets. The paper also discusses the conditions under which PELT can achieve linear computational complexity and provides theoretical results supporting its efficiency.