Dimension Reduction in Dissimilarity Spaces for Time Series Classification

Time series classification in the dissimilarity space combines the advantages of elastic dissimilarity functions such as the dynamic time warping distance and the rich mathematical structure of Euclidean spaces. We applied dimension reduction using PCA followed by support vector learning on dissimilarity representations to 42 UCR datasets. The results suggest that time series classification in dissimilarity space has potential to complement the state-of-the-art, because the SVM classifiers perform better on the 42 datasets with higher confidence than the nearest-neighbor classifier based on the dynamic time warping distance.