Dynamic programming approach for segmentation of multivariate time series

Guo, Hongyue; Liu, Xiaodong; Song, Lixin

doi:10.1007/s00477-014-0897-0

Cited by 18 publications

(12 citation statements)

References 31 publications

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“…Recently, there has been a renewed interest in DP approaches to solve MTS segmentation problems. Guo et al [17] introduce a threshold autoregressive model to define the segment error function of the optimization. However, choosing the ideal value of both segmentation order and autoregressive order is non-trivial.…”

Section: Related Workmentioning

confidence: 99%

“…Orion works in Euclidean space (which supports versatility in practice), while the approach in [9] strongly relies on the local/global centrality measure used to determine the similarity/distance values for all segment pairs. Additionally, the vector-space model we adopt for the representation of the protos does not incur interpretation issues of the estimated parameters of any segment to be detected (e.g., autoregressive coefficients in [17]). …”

Section: Related Workmentioning

confidence: 99%

“…The additive segment cost formulation allows solving the segmentation problem optimally and efficiently via dynamic programming [17], [2 1]. As we increase the number of seg ments m, however, even beyond the true number of segments, the segmentation error decreases.…”

Section: A Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

Understanding computer usage evolution

Anastasiu

Rashidz

Tagarelli

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Understanding computer usage evolution

Anastasiu

Rashidz

Tagarelli

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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“…In ref. [9], the DP algorithm is extended to segmentation of multivariate time series (DP-MTSS). However, the study's method needs to calculate all possible segmentation errors for such series.…”

Section: Introductionmentioning

confidence: 99%

Hybrid dynamic learning mechanism for multivariate time series segmentation

2020

Statistical Analysis

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To improve the efficiency of segmentation methods for multivariate time series, a hybrid dynamic learning mechanism for such series' segmentation is proposed. First, an incremental clustering algorithm is used to automatically cluster variables of multivariate time series. Second, common factors are extracted from every cluster by a dynamic factor model as an ensemble description of the system. Third, this common factor series is segmented by dynamic programming. The proposed method can potentially segment multivariate time series and not only performs segmentation better on multivariate time series with a large number of variables but also improves the running accuracy and efficiency of the algorithm, especially when analyzing complex datasets.

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“…Further, Gedikli et al (2010a) proposed modified DP approach, combining DP with remaining cost concept to divide long time series. Guo et al (2015) generalized DP approach to segment multivariate time series based on threshold autoregressive model. There are also some other methods to be noted, such as the hidden Markov model proposed by Kehagias (2004) and Kehagias and Fortin (2006), and the ''branch and bound'' type algorithm (AUG) used by Gedikli et al (2010b) to segment long time series.…”

Section: Introductionmentioning

confidence: 99%

Dynamic programming-based optimization for segmentation and clustering of hydrometeorological time series

Guo

Liu

2015

Stoch Environ Res Risk Assess

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In this study, we propose a new segmentation algorithm to partition univariate and multivariate time series, where fuzzy clustering is realized for the segments formed in this way. The clustering algorithm involves a new objective function, which incorporates an extra variable related to segmentation, while dynamic time warping (DTW) is applied to determine distances between nonequal-length series. As optimizing the introduced objective function is a challenging task, we put forward an effective approach using dynamic programming (DP) algorithm. When calculating the DTW distance, a DP-based method is developed to reduce the computational complexity. In a series of experiments, both synthetic and real-world time series are used to evaluate the performance of the proposed algorithm. The results demonstrate higher effectiveness and advantages of the constructed algorithm when compared with the existing segmentation approaches.

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Dynamic programming approach for segmentation of multivariate time series

Cited by 18 publications

References 31 publications

Understanding computer usage evolution

Understanding computer usage evolution

Hybrid dynamic learning mechanism for multivariate time series segmentation

Dynamic programming-based optimization for segmentation and clustering of hydrometeorological time series

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