A Study on Time-series Clustering Analysis based on Dynamic Time Warping

2018 ◽  
Vol 20 (5) ◽  
pp. 2319-2332
Author(s):  
Seong Tae Kim ◽  
Man Sik Park
Keyword(s):  
Time Series ◽  
Clustering Analysis ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Time Series Clustering ◽  
Dynamic Time

scholarly journals Efficient Time Series Clustering by Minimizing Dynamic Time Warping Utilization

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 46589-46599
Author(s):  
Borui Cai ◽  
Guangyan Huang ◽  
Najmeh Samadiani ◽  
Guanghui Li ◽  
Chi-Hung Chi
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Time Series Clustering ◽  
Dynamic Time

MODIS NDVI time series clustering under dynamic time warping

2014 ◽  
Vol 12 (05) ◽  
pp. 1461011 ◽  
Author(s):  
Zheng Zhang ◽  
Ping Tang ◽  
Lianzhi Huo ◽  
Zengguang Zhou
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Clustering Algorithm ◽  
Experimental Result ◽  
Time Warping ◽  
Time Series Clustering ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Time Distortion ◽  
Dynamic Time

For MODIS NDVI time series with cloud noise and time distortion, we propose an effective time series clustering framework including similarity measure, prototype calculation, clustering algorithm and cloud noise handling. The core of this framework is dynamic time warping (DTW) distance and its corresponding averaging method, DTW barycenter averaging (DBA). We used 12 years of MODIS NDVI time series to perform annual land-cover clustering in Poyang Lake Wetlands. The experimental result shows that our method performs better than classic clustering based on ordinary Euclidean methods.

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