Dynamic Time Warping Clustering with LB_Keogh

This is an example repository for how to cluster time series using DTW with the LB_Keogh lower-bounding method. This implementation uses K-Means clustering (model as DTWClustering), an unsupervised clustering approach. If enough interest arises, I will show how to perform supervised KNN using these computations.

Citations

1. Keogh, E. (2002). Exact indexing of dynamic time warping. In 28th International Conference on Very Large Data Bases. Hong Kong. pp 406-417.
2. Sakoe, Hiroaki; Chiba, Seibi (1978). "Dynamic programming algorithm optimization for spoken word recognition". IEEE Transactions on Acoustics, Speech, and Signal Processing. 26 (1): 43–49.
3. http://alexminnaar.com/2014/04/16/Time-Series-Classification-and-Clustering-with-Python.html