Abstract
The Dynamic Time Warping (DTW) algorithm is widely used in finding the global alignment of time series. Many time series data mining and analytical problems can be solved by the DTW algorithm. However, using the DTW algorithm to find similar subsequences is computationally expensive or unable to perform accurate analysis. Hence, in the literature, the parallelisation technique is used to speed up the DTW algorithm. However, due to the nature of DTW algorithm, parallelizing this algorithm remains an open challenge. In this paper, we first propose a novel method that finds the similar local subsequence. Our algorithm first searches for the possible start positions of subsequence, and then finds the best-matching alignment from these positions. Moreover, we parallelize the proposed algorithm on GPUs using CUDA and further propose an optimization technique to improve the performance of our parallelization implementation on GPU. We conducted the extensive experiments to evaluate the proposed method. Experimental results demonstrate that the proposed algorithm is able to discover time series subsequences efficiently and that the proposed GPU-based parallelization technique can further speedup the processing.
Highlights
Time series data is a series of values sampled at time intervals[1]
As suggested in Refs. [6,7], many applications can be solved by finding the common subsequence pairs in two time series data, for example, finding the association rules in time series data[8,9], classification algorithms that are based on building typical prototypes of each class[10,11], anomaly detection[12], and finding periodic patterns[13]
Since the classic Dynamic Time Warping (DTW) algorithm serves as the foundation of our proposed algorithm in this paper, we introduce the algorithm briefly
Summary
Time series data is a series of values sampled at time intervals[1]. Dynamic Time Warping (DTW)[14] is a well-known algorithm for comparing similarities between two time series data. A time series can be represented as T D .t1; t2; : : : ; tn/ which denotes ordered values in a time series of n samples[14]. The classic DTW algorithm is based on dynamic programming technique, which stores the previous computed results in a matrix so that the later calculation can use these results directly without re-computing them. The value represents the DTW distance between the time series starting from the first data point up to the corresponding alignment point
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