KHELIL ,HindBOUGUEROUA, Salah2025-11-102025-11-102025http://dspace.univ-skikda.dz:4000/handle/123456789/5351Time series data, characterized by sequential measurements over time, are fundamental in various domains such as finance, healthcare, and environmental monitoring. The increasing volume of time series data, driven by IoT and sensor technologies, necessitates efficient and accurate analysis methods. However, challenges such as high dimensionality, noise, temporal misalignments, and varying lengths complicate classification tasks. This thesis investigates prominent time series classification techniques, evaluating methods including Euclidean distance, Dynamic Time Warping (DTW), Longest Common Subsequence (LCSS), and the Shape Exchange Algorithm (SEA) using the 1-Nearest Neighbor (1NN) classifier on 85 datasets from the UCR Archive. We propose two novel variants, PAA-SEA and RDP-SEA, though experiments confirm the superiority of the original SEA. Additionally, we explore a hybrid DTW-LCSS approach, which outperforms standalone methods. We also assess the previously untested MSR (Mean Squared Residue) method in this field, demonstrating competitive accuracy.enMasters degree thesis