Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining

Cassisi, Carmelo; Montalto, P.; Aliotta, Marco; Cannata, Andrea; Pulvirenti, Alfredo

doi:10.5772/49941

Cited by 125 publications

(92 citation statements)

References 47 publications

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“…This is prevented by forcing the warping path to stay within a region around the diagonal of matrixD [19]. Elements can only be matched if they are within δ temporal units,…”

Section: ) Dynamic Time Warpingmentioning

confidence: 99%

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Investigating similarity measures for locomotor trajectories based on the human perception of differences in motions

Turnwald

Eger

Wollherr

2015

2015 IEEE International Workshop on Advanced Robotics and Its Social Impacts (ARSO)

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Providing robots with the ability to move humanlike is one of the recent challenges for researchers who work on motion planning in human populated environments. Humanlike motions help a human interaction partner to intuitively grasp the intention of the robot. However, the problem of validating the degree of human-likeness of a robot motion is rarely addressed, especially for the forward motion during navigation. One approach is using similarity measures to compare the robot trajectories directly with human ones. For this reason, this paper investigates different methods from the time series analysis that can be applied to measure the similarity between trajectories: the average Euclidean distance, the Dynamic Time Warping distance, and the Longest Common Subsequence. We aim to identify the measure that performs the same way as a human who rates the similarity. Thus, the evaluation of the methods is based on a questionnaire that examines the human perception of differences between walking motions. It is concluded that the human similarity perception is reproduced best by using the Dynamic Time Warping and comparing the derivatives of the path and velocity profiles instead of the absolute values.

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“…This is prevented by forcing the warping path to stay within a region around the diagonal of matrixD [19]. Elements can only be matched if they are within δ temporal units,…”

Section: ) Dynamic Time Warpingmentioning

confidence: 99%

“…Additionally, the warping path has to fulfill the continuity and monotonicity constraints (compare [19]). Dynamic programming is used to find the warping path: based on the distance matrix D, a cumulative matrixD is computed with the elements…”

Section: ) Dynamic Time Warpingmentioning

confidence: 99%

Investigating similarity measures for locomotor trajectories based on the human perception of differences in motions

Turnwald

Eger

Wollherr

2015

2015 IEEE International Workshop on Advanced Robotics and Its Social Impacts (ARSO)

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“…There are a variety of distance metrics to measure how close the two vectors are. Assuming vectors

\overset{X}{true}

and

\overset{Y}{true}

can be written as

\begin{matrix} lefttrue \\ \overset{false\to}{X} = [x_{1}, x_{2}, x_{3}, \dots, x_{m}], \\ \overset{false\to}{Y} = [y_{1}, y_{2}, y_{3}, \dots, y_{m}], \end{matrix}

four measures of distance are described later.…”

Section: Batch Similaritymentioning

confidence: 99%

Application of a kNN‐based similarity method to biopharmaceutical manufacturing

Ren

Zhou

Farrow

et al. 2019

Biotechnology Progress

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Machine learning‐based similarity analysis is commonly found in many artificial intelligence applications like the one utilized in e‐commerce and digital marketing. In this study, a kNN‐based (k‐nearest neighbors) similarity method is proposed for rapid biopharmaceutical process diagnosis and process performance monitoring. Our proposed application measures the spatial distance between batches, identifies the most similar historical batches, and ranks them in order of similarity. The proposed method considers the similarity in both multivariate and univariate feature spaces and measures batch deviations to a benchmarking batch. The feasibility and effectiveness of the proposed method are tested on a drug manufacturing process at Biogen.

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“…The classical foundations of the problem of identifying similarities and their classification have been described in [2,3]. In [4], basic theses of the problem of search for similarities are described by comparison with a sample, the methods of calculating metric distances, the problem of segmentation and approximation of time series are described.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of adaptive combined models for predicting time series based on similarity identification

Kuchansky

Biloshchytskyi

Andrashko

et al. 2018

EEJET

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23. Neural crest and mesoderm lineage-dependent gene expression in orofacial development / Bhattacherjee V., Mukhopadhyay P., Singh S., Johnson C., Philipose J. T., Warner C. P. DEVELOPMENT OF ADAPTIVE COMBINED MODELS FOR PREDICTING TIME SERIES BASED ON SIMILARITY IDENTIFICATION A . K u c h a n s k yPhD, Associate Professor Department of Cybersecurity and Computer Engineering* E-mail: kuczanski@gmail.com A . B i l o s h c h y t s k y i Y u . A n d r a s h k oLecturer Department of System Analysis and Optimization Theory Uzhhorod National University Narodna sq., 3, Uzhhorod, Ukraine, 88000 S . B i l o s h c h y t s k a

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Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining

Cited by 125 publications

References 47 publications

Investigating similarity measures for locomotor trajectories based on the human perception of differences in motions

Investigating similarity measures for locomotor trajectories based on the human perception of differences in motions

Application of a kNN‐based similarity method to biopharmaceutical manufacturing

Development of adaptive combined models for predicting time series based on similarity identification

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