TD_DTW (Dynamics Time Warping) measures the similarity of two temporal sequences that vary in speed or timing. It is useful when comparing two time series that have similar shapes but are shifted, scaled differently, or have different lengths. The function uses the FastDTW algorithm.
TD_DTW aligns the sequences by warping the time axis of one sequence to match the other, allowing for point-to-point comparisons at corresponding times. The function computes a matrix that quantifies the similarity between each point in one sequence and each point in the other sequence. It then finds the optimal path in the matrix that minimizes the total cost of warping the time axis.
TD_DTW is used in speech recognition, signal processing, and bioinformatics. For example, it can compare the similarity of two spoken words or to match a pattern in a noisy signal. It is also used in clustering or classification problems, where the temporal structure of the data needs to be taken into account.