Time Series Dynamic Time Warping Algorithm Implementation

This article presents a MATLAB-implemented dynamic time warping algorithm for time series analysis. The algorithm serves as a powerful tool for understanding and analyzing time series data, enabling the discovery of underlying patterns and trends. Our implementation features high flexibility and customizability, allowing users to adapt and optimize the algorithm according to specific requirements through parameter adjustments and distance metric modifications. The core algorithm employs dynamic programming to compute the optimal alignment path between two time series sequences, utilizing functions such as pdist2 for distance calculations and custom path reconstruction routines. The package includes detailed documentation covering function signatures, input/output parameters, and practical usage scenarios. Key components include the main DTW calculation function, visualization utilities for warping paths, and performance optimization techniques for handling large datasets. Users can leverage built-in examples demonstrating applications in signal processing, financial data analysis, and pattern recognition. This comprehensive toolkit provides researchers and analysts with robust capabilities for advanced time series data interpretation and utilization.