Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Exact indexing of dynamic time warping
Knowledge and Information Systems
Clustering of time-series subsequences is meaningless: implications for previous and future research
Knowledge and Information Systems
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
An efficient and accurate method for evaluating time series similarity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
On Clustering Multimedia Time Series Data Using K-Means and Dynamic Time Warping
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
Wavelet/mixture of experts network structure for EEG signals classification
Expert Systems with Applications: An International Journal
Clustering Time Series with Granular Dynamic Time Warping Method
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
An Incremental Algorithm for Clustering Search Results
SITIS '08 Proceedings of the 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems
Faster retrieval with a two-pass dynamic-time-warping lower bound
Pattern Recognition
A time series representation model for accurate and fast similarity detection
Pattern Recognition
Spectral preprocessing for clustering time-series gene expressions
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
A shapelet transform for time series classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Alternative quality measures for time series shapelets
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Time series classification by class-specific Mahalanobis distance measures
Advances in Data Analysis and Classification
Possibilistic nonlinear dynamical analysis for pattern recognition
Pattern Recognition
A time series forest for classification and feature extraction
Information Sciences: an International Journal
The influence of global constraints on similarity measures for time-series databases
Knowledge-Based Systems
On-line signature verification using vertical signature partitioning
Expert Systems with Applications: An International Journal
Classification of time series by shapelet transformation
Data Mining and Knowledge Discovery
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Dynamic time warping (DTW), which finds the minimum path by providing non-linear alignments between two time series, has been widely used as a distance measure for time series classification and clustering. However, DTW does not account for the relative importance regarding the phase difference between a reference point and a testing point. This may lead to misclassification especially in applications where the shape similarity between two sequences is a major consideration for an accurate recognition. Therefore, we propose a novel distance measure, called a weighted DTW (WDTW), which is a penalty-based DTW. Our approach penalizes points with higher phase difference between a reference point and a testing point in order to prevent minimum distance distortion caused by outliers. The rationale underlying the proposed distance measure is demonstrated with some illustrative examples. A new weight function, called the modified logistic weight function (MLWF), is also proposed to systematically assign weights as a function of the phase difference between a reference point and a testing point. By applying different weights to adjacent points, the proposed algorithm can enhance the detection of similarity between two time series. We show that some popular distance measures such as DTW and Euclidean distance are special cases of our proposed WDTW measure. We extend the proposed idea to other variants of DTW such as derivative dynamic time warping (DDTW) and propose the weighted version of DDTW. We have compared the performances of our proposed procedures with other popular approaches using public data sets available through the UCR Time Series Data Mining Archive for both time series classification and clustering problems. The experimental results indicate that the proposed approaches can achieve improved accuracy for time series classification and clustering problems.