Distance measure with improved lower bound for multivariate time series

Li, Hailin

doi:10.1016/j.physa.2016.10.062

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…The previously proposed optimizations have concentrated on the reduction or transformations of the high dimension space. Li and others [23,24] tries to reduce all dimensions to a single dimension (center series) and then use univariate methods; Hu and others have studied the effects of giving more emphasis on important dimensions [14]; Gong and others demonstrate that by rotating the space, one could improve the tightness of the lower bounds [13]. Some optimizations proposed for univariate DTW could potentially benefit applications of multivariate DTW.…”

Section: Related Workmentioning

confidence: 99%

“…It was proposed in 2003 as a straightforward extension of the univariate lower bound-based DTW algorithms to multivariate data. There have been a few attempts to speed up multivariate DTW, but they are primarily concentrated on the reduction or transformations of the high dimension problem space [13,14,23,24], rather than optimizing the designs of DTW algorithms.…”

Section: Introductionmentioning

confidence: 99%

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TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle Inequality and Point Clustering

Shen¹,

Chi

2021

Preprint

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Dynamic time warping (DTW) plays an important role in analytics on time series. Despite the large body of research on speeding up univariate DTW, the method for multivariate DTW has not been improved much in the last two decades. The most popular algorithm used today is still the one developed seventeen years ago. This paper presents a solution that, as far as we know, for the first time consistently outperforms the classic multivariate DTW algorithm across dataset sizes, series lengths, data dimensions, temporal window sizes, and machines. The new solution, named TC-DTW, introduces Triangle Inequality and Point Clustering into the algorithm design on lower bound calculations for multivariate DTW. In experiments on DTW-based nearest neighbor finding, the new solution avoids as much as 98% (60% average) DTW distance calculations and yields as much as 25× (7.5× average) speedups.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle Inequality and Point Clustering

Shen¹,

Chi

2021

Preprint

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“…In addition, it is unsensitive to the abnormal data points and can deal with the similarity between two time series with different lengths. Due to the merits of DTW [35], [38], [39], it is often applied to the field of time series data mining such as gesture recognition, speech processing, image recognition and medicine expert systems.…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

“…They are used to limit the scope of the cells surrounding the optimal warping path in the cost matrix. In especial, the Sakoe-Chiba Band is widely used in the community of similarity search for time series [35], [38]. As shown in Figure.2, the best warping path in red is only in the scope of the green cells that constructed by a warping window l. In this way, DTW based on the warping window can be faster than the traditional one.…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

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Similarity Measure Based on Incremental Warping Window for Time Series Data Mining

Wang

2019

IEEE Access

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A similarity measure is one of the most important tasks in the fields of time series data mining. Its quality often affects the efficiency and effectiveness of the related algorithms that need to measure the similarity between two time series in advance. Dynamic time warping is one of the most robust methods to compare one time series with another based on warping alignments. In this paper, the design of an incremental warping window is used to improve the performance of dynamic time warping. The incremental warping window is changeable for various time series with different lengths. Moreover, the improved dynamic time warping based on the novel window considers the recent alignments as much as possible, which indicates that the proposed method concentrates on more information of the recent data points than that of the previous data points. In addition, it is suitable for online similarity measure between data stream. The experimental evaluation shows that the proposed method is effective and efficient for time series mining. INDEX TERMS Dynamic time warping, similarity measure, time series data mining, incremental warping window, classification.

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Dimensionality reduction for multivariate time-series data mining

Wan

Zhang

et al. 2022

J Supercomput

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Distance measure with improved lower bound for multivariate time series

Cited by 10 publications

References 21 publications

TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle Inequality and Point Clustering

TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle Inequality and Point Clustering

Similarity Measure Based on Incremental Warping Window for Time Series Data Mining

Dimensionality reduction for multivariate time-series data mining

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