Dynamic Time Warping (DTW)

Dynamic Time Warping (DTW) is a technique used to measure similarity between two time-dependent sequences, like speech patterns or stock prices. It aligns the sequences in a flexible way, allowing for shifts and stretches in time, so similar patterns can be matched even if they occur at different speeds or times. This helps identify how closely two sequences resemble each other, accounting for variations in timing. DTW is widely used in fields like speech recognition, gesture analysis, and data comparison to find meaningful similarities despite temporal differences.