What is dynamic time warping used for?

What is dynamic time warping used for?

Dynamic Time Warping is used to compare the similarity or calculate the distance between two arrays or time series with different length.

How do you cluster time series data?

Clustering, which is one of the most important concepts of data mining, defines its structure by separating unlabeled data sets into homogeneous groups. Many general-purpose clustering algorithms are used for the clustering of time-series data, either by directly or by evolving.

What is DTW clustering?

When grouping time series based on their shape information is of interest (shape-based clustering), using a Dynamic Time Warping (DTW) distance is a desirable choice. Using stretching or compressing segments of temporal data, DTW determines an optimal match between any two time series.

How is dynamic time warping clustering validated?

Dynamic Time Warp compares the realized data points, which may or may not work. A more rigorous approach is to compare the distribution of the time series by way of a metric called telescope distance. The cool thing about this metric is that the empirical calculation is done by fitting a series of binary classifiers such as SVM.

How to apply clustering to time series data?

Intuitively, the distance measures used in standard clustering algorithms, such as Euclidean distance, are often not appropriate to time series. A better approach is to replace the default distance measure with a metric for comparing time series, such as Dynamic Time Warping.

How is DTW barycenter averaging used in clustering?

A recent method DTW Barycenter Averaging (DBA) has been proposed by Petitjean et al. to average time series. In an other paper they proved empirically and theoretically how it can be used to cluster time series with k-means. An implementation is provided on GitHub by the authors ( link to code ).

How to run hierarchical clustering using DTW?

Instead, compute a distance matrix using DTW, then run hierarchical clustering such as single-link. In contrast to k-means, the series may even have different length.