Recent Advances in Time-Series Classification—Methodology and Applications
Recent Advances in Time-Series Classification—Methodology and Applications
English | 2025 | ISBN: 3031775260 | 334 Pages | PDF (True) | 26 MB
English | 2025 | ISBN: 3031775260 | 334 Pages | PDF (True) | 26 MB
This book examines the impact of such constraints on elastic time-series similarity measures and provides guidance on selecting suitable measures. Time-series classification frequently relies on selecting an appropriate similarity or distance measure to compare time series effectively, often using dynamic programming techniques for more robust results. However, these techniques can be computationally demanding, which results in the usage of global constraints to reduce the search area in the dynamic programming matrix. While these constraints cut computation time significantly (by up to three orders of magnitude), they may also affect classification accuracy.