Authors:
Mohammed Alshehri
1
;
Frans Coenen
2
and
Keith Dures
2
Affiliations:
1
Department of Computer Science, University of Liverpool, Liverpool, U.K., Department of Computer Science, King Khalid University, Abha and Saudi Arabia
;
2
Department of Computer Science, University of Liverpool, Liverpool and U.K.
Keyword(s):
Time Series Analysis, Dynamic Time Warping, k-Nearest Neighbor Classification, Splitting Method.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Computational Intelligence
;
Data Analytics
;
Data Engineering
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
In time series classification the most commonly used approach is k Nearest Neighbor classification, where k = 1, coupled with Dynamic Time Warping (DTW) similarity checking. A challenge is that the DTW process is computationally expensive. This paper presents a new approach for speeding-up the DTW process, Sub-Sequence-Based DTW, which offers the additional benefit of improving accuracy. This paper also presents an analysis of the impact of the Sub-Sequence-Based method in terms of efficiency and effectiveness in comparison with standard DTW and the Sakoe-Chiba Band technique.