A Shape Based Similarity Measure for Time Series Classification with Weighted Dynamic Time Warping Algorithm

Ye, Yanqing; Niu, Caiyun; Jiang, Jiang; Ge, Bingfeng; Yang, Kewei

doi:10.1109/icisce.2017.32

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…The WDTW penalizes points with higher phase difference between a reference point and a testing point in order to prevent minimum distance distortion caused by outliers. In [34], the authors proposed a shape based similarity measure by introducing a shape coefficient into the WDTW algorithm.…”

Section: Similarity Measuresmentioning

confidence: 99%

Shape-based Representation and Abstraction of Time Series Data along with a Dynamic Time Shape Wrapping as a Dissimilarity Measure

Gmati

Chakhar

Chaari

et al. 2021

2021 26th International Conference on Automation and Computing (ICAC)

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This paper proposes a Time Series Shape (TSS) based framework for time series representation and abstraction. The paper also introduces a Dynamic Time Shape Wrapping (DTSW), which is a shape extension of the well-known Dynamic Time Wrapping (DTW) dissimilarity measure. By jointly supporting representation and abstraction, TSS and its related dissimilarity measure DTSW can be applied in hybrid time series data mining tasks, especially those involving both rule induction and classification. The paper also compares the capabilities of TSS and piecewise aggregate approximation (PAA) representation in a classification task. Results show that TSS has the same dimensionality reduction power as PAA. This means that TSS is able to maintain the same classification accuracy as PAA, with an additional time series abstraction capability. The results also indicate that DTSW is able to successfully quantify the comparison between TSS abstractions.

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Section: Similarity Measuresmentioning

confidence: 99%

Shape-based Representation and Abstraction of Time Series Data along with a Dynamic Time Shape Wrapping as a Dissimilarity Measure

Gmati

Chakhar

Chaari

et al. 2021

2021 26th International Conference on Automation and Computing (ICAC)

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show abstract

“…Ye et al [21] presented a shape based similarity measure. By means of presenting a shape coefficient into the traditional weighted dynamic time warping algorithm, an improved version, Shape based Weighted Dynamic Time Warping (SWDTW) algorithm is introduced.…”

Section: Related Workmentioning

confidence: 99%

Optimal and efficient time series classification with burrows-wheeler transform and spectral window based transformation

Karthikeyan¹,

Sitamahalakshmi²

2018

PEN

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With the progressing amount of data every day, Time series classification acts as a vital role in the real life environment. Raised data volume for the time periods will make hard for the researchers to examine as well as assess the data. Therefore time series classification is taken as a significant research problem for the examining as well as identifying the time series dataset. On the other hand the previous research might carry out low in case of existence of weak classifiers. It is solved by introducing the Weak Classifier aware Time Series Data Classification Algorithm (WCTSD). In this proposed technique, with the help of the Burrows-Wheeler Transform (BWT), primarily frequency domain based data transformation is carried out. After that, by means of presenting the technique called spectral window based transformation, time series based data transformation is performed. With the help of the Hybrid K Nearest Neighbour, Hybrid decision tree algorithm, Linear Multiclass Support Vector Machine, these transformed data is classified. Here, to enhance the classification accuracy, the weak classifier is eliminated by utilizing hybrid particle swarm with firefly algorithm. In the MATLAB simulation environment, the total implementation of the presented research technique is carried out and it is confirmed that the presented research technique WCTSD results in providing the best possible outcome compared to the previous research methods.

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A Clustering-Based Framework for Understanding Individuals’ Travel Mode Choice Behavior

Zhao

Bucher

Henry

et al. 2019

Lecture Notes in Geoinformation and Cartography

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A Shape Based Similarity Measure for Time Series Classification with Weighted Dynamic Time Warping Algorithm

Cited by 7 publications

References 10 publications

Shape-based Representation and Abstraction of Time Series Data along with a Dynamic Time Shape Wrapping as a Dissimilarity Measure

Shape-based Representation and Abstraction of Time Series Data along with a Dynamic Time Shape Wrapping as a Dissimilarity Measure

Optimal and efficient time series classification with burrows-wheeler transform and spectral window based transformation

A Clustering-Based Framework for Understanding Individuals’ Travel Mode Choice Behavior

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