How do dynamic time warping (DTW) algorithms apply to time series analysis?
Instruction: Explain the principle of DTW and its application in comparing time series.
Updated
Example Answer
DTW compares sequences by allowing local stretches or compressions of their time alignment. A dynamic-programming algorithm finds a low-cost path through the pairwise distance matrix, subject to ordering and path constraints. It is useful when similar shapes occur at different speeds, such as two recordings of the same gesture.
I would choose the local distance and normalization based on the task. DTW does not automatically make arbitrary amplitude differences irrelevant. I would also constrain the warping window so an implausible alignment cannot make unrelated sequences appear similar. The resulting distance is a similarity measure under those choices, not a causal finding. I would test it against known similar and dissimilar examples before using it for clustering or retrieval.
Make it your own
Choose a pair of sequences, explain whether magnitude matters, and define an acceptable timing distortion.
Why this works
It explains both the alignment mechanism and its limits.
Follow-up to practice
What happens if the warping window is too wide?