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Alternative Quality Measures for Time Series Shapelets [chapter]

Jason Lines, Anthony Bagnall
<span title="">2012</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
One of the most promising approaches proposed for TSC is time series shapelets.  ...  Classification is a very broad and prevalent topic of research within data mining. Whilst heavily related, time series classification (TSC) offers a more specific challenge.  ...  For the problem of time series classification, suppose we have a set of n time series T = T 1 , T 2 , ..., T n , where each time series t has m real-value ordered readings T i =< t i,1 , t i,2 , ..., t  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-32639-4_58">doi:10.1007/978-3-642-32639-4_58</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ivw4qfklwnh7vi5h4zomsyhevm">fatcat:ivw4qfklwnh7vi5h4zomsyhevm</a> </span>
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Fast Randomized Model Generation for Shapelet-Based Time Series Classification [article]

Daniel Gordon, Danny Hendler, Lior Rokach
<span title="2012-09-23">2012</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A shapelet is a subsequence extracted from one of the time series in the dataset. A disadvantage of this approach is the time required for building the shapelet-based classification tree.  ...  Time series classification is a field which has drawn much attention over the past decade. A new approach for classification of time series uses classification trees based on shapelets.  ...  A new approach for classification of time series, proposed by Ye and Keogh [11] , uses shapelets. A shapelet is a subsequence extracted from one of the time series in the dataset.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1209.5038v1">arXiv:1209.5038v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k6bg7zf4xnaefp4mw2dh62wk5i">fatcat:k6bg7zf4xnaefp4mw2dh62wk5i</a> </span>
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Random Pairwise Shapelets Forest [article]

Mohan Shi, Zhihai Wang, Jodong Yuan, Haiyang Liu
<span title="2019-04-22">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Shapelet is a discriminative subsequence of time series. An advanced shapelet-based method is to embed shapelet into accurate and fast random forest. However, it shows several limitations.  ...  Second, a single shapelet provides limited information for only one branch of the decision tree, resulting in insufficient accuracy and interpretability.  ...  Random shapelet forest has also been extended to multivariate time series forest, applied successfully to ECG classification [9] and early classification problem [10] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1903.07799v2">arXiv:1903.07799v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hjzpu753c5gmvbygr7tss5gutq">fatcat:hjzpu753c5gmvbygr7tss5gutq</a> </span>
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Enhancing statistical power in temporal biomarker discovery through representative shapelet mining

Thomas Gumbsch, Christian Bock, Michael Moor, Bastian Rieck, Karsten Borgwardt
<span title="2020-12-30">2020</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wmo54ba2jnemdingjj4fl3736a" style="color: black;">Bioinformatics</a> </i> &nbsp;
The search for shapelets requires considering all subsequences in the data.  ...  Structural diversity is achieved by pruning non-representative shapelets via submodular optimization.  ...  Traditionally, shapelets serve as a frequency-based feature extraction approach for time series subsequences enabling competitive classification accuracy (Karlsson et al., 2016) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/btaa815">doi:10.1093/bioinformatics/btaa815</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33381811">pmid:33381811</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7bjwoh7vkrgpnk3qguicu25iye">fatcat:7bjwoh7vkrgpnk3qguicu25iye</a> </span>
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A shapelet transform for time series classification

Jason Lines, Luke M. Davis, Jon Hills, Anthony Bagnall
<span title="">2012</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqqihtxlu5bvfaqxjyvqcob35a" style="color: black;">Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD &#39;12</a> </i> &nbsp;
One of the most promising recent approaches is to find shapelets within a data set. A shapelet is a time series subsequence that is identified as being representative of class membership.  ...  The problem of time series classification (TSC), where we consider any real-valued ordered data a time series, presents a specific machine learning challenge as the ordering of variables is often crucial  ...  For the problem of time series classification, suppose we have a set of n time series, T = {T1, T2, ..., Tn}, where each time series has m ordered real-valued observations Ti =< ti1, ti2, ..., tim > and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2339530.2339579">doi:10.1145/2339530.2339579</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/kdd/LinesDHB12.html">dblp:conf/kdd/LinesDHB12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jb6qxtyg3rd3jdn4gcgjqg4c3a">fatcat:jb6qxtyg3rd3jdn4gcgjqg4c3a</a> </span>
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Adversarial Dynamic Shapelet Networks

Qianli Ma, Wanqing Zhuang, Sen Li, Desen Huang, Garrison Cottrell
<span title="2020-04-03">2020</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wtjcymhabjantmdtuptkk62mlq" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;
Shapelets are discriminative subsequences for time series classification. Recently, learning time-series shapelets (LTS) was proposed to learn shapelets by gradient descent directly.  ...  Experiments conducted on extensive time series data sets show that ADSN is state-of-the-art compared to existing shapelet-based methods.  ...  Acknowledgments We thank the anonymous reviewers for their helpful feedbacks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i04.5948">doi:10.1609/aaai.v34i04.5948</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/z4spt7277bhlbktin3ue2vcdlm">fatcat:z4spt7277bhlbktin3ue2vcdlm</a> </span>
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Classification of time series by shapelet transformation

Jon Hills, Jason Lines, Edgaras Baranauskas, James Mapp, Anthony Bagnall
<span title="2013-05-18">2013</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ja54f4dcmzfz7cvlo7o757jdba" style="color: black;">Data mining and knowledge discovery</a> </i> &nbsp;
Abstract Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series.  ...  A shapelet is a time-series subsequence that allows for TSC based on local, phase-independent similarity in shape.  ...  Early abandon of the distance calculations for shapelet S and series T i .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10618-013-0322-1">doi:10.1007/s10618-013-0322-1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zsl2ccehqjbbdbewf2pfpyeywq">fatcat:zsl2ccehqjbbdbewf2pfpyeywq</a> </span>
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Time series shapelets: a novel technique that allows accurate, interpretable and fast classification

Lexiang Ye, Eamonn Keogh
<span title="2010-06-18">2010</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ja54f4dcmzfz7cvlo7o757jdba" style="color: black;">Data mining and knowledge discovery</a> </i> &nbsp;
As we shall show with extensive empirical evaluations in diverse domains, classification algorithms based on the time series shapelet primitives can be interpretable, more accurate, and significantly faster  ...  In this work we introduce a new time series primitive, time series shapelets, which addresses these limitations.  ...  Acknowledgements We thank the reviewers for their helpful comments and suggestions. We thank Ralf Hartemink for help with the heraldic shields and Dr.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10618-010-0179-5">doi:10.1007/s10618-010-0179-5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xmvd3t2oonbt7hnhfdjybtmkmu">fatcat:xmvd3t2oonbt7hnhfdjybtmkmu</a> </span>
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GENDIS: Genetic Discovery of Shapelets

Gilles Vandewiele, Femke Ongenae, Filip De Turck
<span title="2021-02-04">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
In the time series classification domain, shapelets are subsequences that are discriminative of a certain class.  ...  In this study, a new paradigm for shapelet discovery is proposed, which is based on evolutionary computation.  ...  [10] compared their technique to 36 other algorithms for time series classification on 85 datasets [13] , which showed that their technique is one of the top-performing algorithms for time series classification  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21041059">doi:10.3390/s21041059</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33557169">pmid:33557169</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7913966/">pmcid:PMC7913966</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cvjmaac5gbepbcnyjbdejwsm4q">fatcat:cvjmaac5gbepbcnyjbdejwsm4q</a> </span>
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Technology investigation on time series classification and prediction

Yuerong Tong, Jingyi Liu, Lina Yu, Liping Zhang, Linjun Sun, Weijun Li, Xin Ning, Jian Xu, Hong Qin, Qiang Cai
<span title="2022-05-18">2022</span> <i title="PeerJ"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zs2czkfyybggbpvr26rbyxpsjy" style="color: black;">PeerJ Computer Science</a> </i> &nbsp;
For time series classification, it is divided into supervised methods, semi-supervised methods, and early classification of time series, which are key extensions of time series classification tasks.  ...  A statistical analysis of 120,000 literatures published between 2017 and 2021 reveals that the topical research about time series is mostly focused on their classification and prediction.  ...  Technology developments Based on the literature reviewed, we discover three development routes: supervised time series classification, semi-supervised time series classification, and early classification  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7717/peerj-cs.982">doi:10.7717/peerj-cs.982</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35634126">pmid:35634126</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC9138170/">pmcid:PMC9138170</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nzwe4ugliza4jgroqve4qrxtwq">fatcat:nzwe4ugliza4jgroqve4qrxtwq</a> </span>
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Time series shapelets

Lexiang Ye, Eamonn Keogh
<span title="">2009</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqqihtxlu5bvfaqxjyvqcob35a" style="color: black;">Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD &#39;09</a> </i> &nbsp;
As we shall show with extensive empirical evaluations in diverse domains, algorithms based on the time series shapelet primitives can be interpretable, more accurate and significantly faster than state-of-the-art  ...  Classification of time series has been attracting great interest over the past decade.  ...  Acknowledgements: We thank Ralf Hartemink for help with the heraldic shields and Dr. Sang-Hee Lee and Taryn Rampley for their help with the projectile point dataset.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1557019.1557122">doi:10.1145/1557019.1557122</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/kdd/YeK09.html">dblp:conf/kdd/YeK09</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6w3hojy465bxzjhgokpwtbf3fq">fatcat:6w3hojy465bxzjhgokpwtbf3fq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808205130/http://alumni.cs.ucr.edu/~lexiangy/Shapelet/Shapelet.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9c/40/9c40543348fef37369c5b19ea26994ca1db8d9e8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1557019.1557122"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs [article]

Atif Raza, Stefan Kramer
<span title="2017-02-22">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Consequently, shapelet discovery for large time series datasets quickly becomes intractable. A number of improvements have been proposed to reduce the training time.  ...  Shapelets are discriminative time series subsequences that allow generation of interpretable classification models, which provide faster and generally better classification than the nearest neighbor approach  ...  This leads to an early extraction of the shapelet specific to the instances of the majority class and allows the algorithm to effectively split the dataset early and then search for shapelets for the other  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1702.06712v1">arXiv:1702.06712v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/y4kbat44xfdxvpqatj3aczvgwy">fatcat:y4kbat44xfdxvpqatj3aczvgwy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191024040300/https://arxiv.org/pdf/1702.06712v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/33/29/3329eb9e9d95c571e6fb353d81eb91796f8ec708.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1702.06712v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Time-Variant Graph Classification [article]

Haishuai Wang
<span title="2017-06-12">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs.  ...  To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet  ...  These results imply that graph-shapelet patterns can be used for early prediction.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1609.04350v2">arXiv:1609.04350v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/54hsghecwjhajbgtwmoskftbt4">fatcat:54hsghecwjhajbgtwmoskftbt4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191016093410/https://arxiv.org/pdf/1609.04350v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/be/15/be155437fa50ac8717ff361531843d2f9e508ac4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1609.04350v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Extracting discriminative features for event-based electricity disaggregation

Om P. Patri, Anand V. Panangadan, Charalampos Chelmis, Viktor K. Prasanna
<span title="">2014</span> <i title="IEEE"> 2014 IEEE Conference on Technologies for Sustainability (SusTech) </i> &nbsp;
We describe a novel method for electricity load disaggregation based on the machine learning method of time series shapelets.  ...  We frame the electricity disaggregation problem as that of event detection and event classification from time series data.  ...  ACKNOWLEDGMENTS This work is supported by Chevron Corp. under the joint project, Center for Interactive Smart Oilfield Technologies (CiSoft), at the University of Southern California.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/sustech.2014.7046249">doi:10.1109/sustech.2014.7046249</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6ro43ysabbadrjmo4rqqbkn4be">fatcat:6ro43ysabbadrjmo4rqqbkn4be</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20151022151058/http://halcyon.usc.edu/~pk/prasannawebsite/papers/2014/OM_sustech14.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bd/90/bd90d9ba28f7852302de9b2ea1934ad28df34312.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/sustech.2014.7046249"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

PU-Shapelets: Towards Pattern-Based Positive Unlabeled Classification of Time Series [chapter]

Shen Liang, Yanchun Zhang, Jiangang Ma
<span title="">2019</span> <i title="Springer Singapore"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/z46cpbellzelnpxepfpcj4vwvi" style="color: black;">Complexity in Polish Phonotactics</a> </i> &nbsp;
Most existing time series PU classification methods utilize all readings in the time series, makeing them sensitive to non-characteristic readings.  ...  Real-world time series classification applications often involve positive unlabeled (PU) training data, where there are only a small set P L of positive labeled examples and a large set U of unlabeled  ...  We begin with the concept of positive unlabeled classification [8] . We move on to the definitions of time series and subsequence.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-18576-3_6">doi:10.1007/978-3-030-18576-3_6</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/dasfaa/LiangZM19.html">dblp:conf/dasfaa/LiangZM19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ctzvxlfrlfa5jf7bmkzm3skmdu">fatcat:ctzvxlfrlfa5jf7bmkzm3skmdu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200305080045/https://researchonline.federation.edu.au/vital/access/services/Download/vital:13897/SOURCE2" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d3/ef/d3ef20defb8869fdea1ee9ffa7af646f4a64fb2f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-18576-3_6"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>
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