Dynamic Time Warping Algorithm in Time Series, Explained - KDnuggets

The phrase "dynamic time warping," at first read, might evoke images of Marty McFly driving his DeLorean at 88 MPH in the Back to the Future series. Alas, dynamic time warping does not involve time travel; instead, it's a technique used to dynamically compare time series data when the time indices between comparison data points do not sync up perfectly. As we'll explore below, one of the most salient uses of dynamic time warping is in speech recognition – determining whether one phrase matches another, even if the phrase is spoken faster or slower than its comparison. You can imagine that this comes in handy to identify the "wake words" used to activate your Google Home or Amazon Alexa device – even if your speech is slow because you haven't yet had your daily cup(s) of coffee. In time series analysis, Dynamic Time Warping (DTW) is one of the algorithms for measuring the similarity between two temporal time series sequences, which may vary in speed.