Dynamic Time Warping (DTW) is an algorithm used to measure the similarity between two sequences that may vary in time or speed. It aligns the sequences in a way that minimizes the distance between them, allowing for flexible matching. This is particularly useful in fields like speech recognition and data mining, where the timing of events can differ. DTW works by creating a cost matrix that calculates the cumulative distance between points in the sequences. By finding the optimal path through this matrix, DTW can effectively compare sequences of different lengths, making it a powerful tool for analyzing time-dependent data, such as audio signals or biometric measurements.