dtw

#Dynamic Time Warping (DTW)

##Description Dynamic Time Warping (DTW) is a powerful classifier that works well for recognizing temporal gestures. Temporal gestures can be defined as a cohesive sequence of movements that occur over a variable time period.

The DTW algorithm is a supervised learning algorithm that can be used to classify any type of N-dimensional, temporal signal. The DTW algorithm works by creating a template time series for each gesture that needs to be recognized, and then warping the realtime signals to each of the templates to find the best match. The DTW algorithm also computes rejection thresholds that enable the algorithm to automatically reject sensor values that are not the K gestures the algorithm has been trained to recognized (without being explicitly told during the prediction phase if a gesture is, or is not, being performed).

You can find out more about the DTW algorithm in Gillian, N. (2011) Recognition of multivariate temporal musical gestures using n-dimensional dynamic time warping.

The DTW algorithm is part of the GRT classification modules.

##Training Data Format You should use the TimeSeriesClassificationData data structure to train the DTW classifier.

##Example DTW Example