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-10.33344622s1.1875777-6.20179466 2.24320232 0c0 0 5.9378885 1.05562462 0 2.11124925 0 0-1.05562463 6.33374774-2.24320233 0-3.5627331-.6597654-3.29882695-1.31953078 0-2.11124925z"></path><path style="animatn:sparkle 1.5s 0.9s fe ease--out" d="M33.5173993 75.97263826s1.03464615-5.40315215 1.95433162 0c0 0 5.17323078.91968547 0 1.83937095 0 0-.91968547 5.51811283-1.95433162 0-3.10393847-.57480342-2.8740171-1.14960684 0-1.83937095z"></path><path style="animatn:sparkle 1.7s 0.4s fe ease--out" d="M69.03038108 1.71240809s.73779281-3.852918 1.39360864 0c0 0 3.68896404.65581583 0 1.31163166 0 0-.65581583 3.93489497-1.39360864 0-2.21337842-.4098849-2.04942447-.81976979 0-1.31163166z"></path></g></svg></div></div><span class="Search_publitn-date__nSb6j">Aug 02, 2023<br/></span><span class="AuthorLks_thors-ntaer__r_iyO"><span><a data-ttid="paper-rult-thor" href="/thor/Aur%C3%A9lien%20Renlt">Aurélien Renlt</a>, </span><span><a data-ttid="paper-rult-thor" href="/thor/Alexis%20Bondu">Alexis Bondu</a>, </span><span><a data-ttid="paper-rult-thor" href="/thor/Vcent%20Lemaire">Vcent Lemaire</a>, </span><span><a data-ttid="paper-rult-thor" href="/thor/Domique%20Gay">Domique Gay</a></span><br/><br/></span><div class="Search_paper-tail-page-imag-ntaer__Wphz5"></div><p class="Search_paper-ntent__eQ9jv">Time Seri Classifitn (TSC) has received much attentn the past two s and is still a ccial and challengg problem data science and knowledge engeerg. Ined, along wh the creasg availabily of time seri data, many TSC algorhms have been suggted by the rearch muny the lerature. Bis state-of-the-art methods based on siary measur, tervals, shapelets, dictnari, ep learng methods or hybrid ensemble methods, several tools for extractg unsupervised rmative summary statistics, aka featur, om time seri have been signed the recent years. Origally signed for scriptive analysis and visualizatn of time seri wh rmative and terpretable featur, very few of the feature engeerg tools have been benchmarked for TSC problems and pared wh state-of-the-art TSC algorhms terms of predictive performance. In this article, we aim at fillg this gap and propose a simple TSC procs to evaluate the potential predictive performance of the feature sets obtaed wh existg feature engeerg tools. Th, we prent an empiril study of 11 feature engeerg tools branched wh 9 supervised classifiers over 112 time seri data sets. The analysis of the rults of more than 10000 learng experiments dite that feature-based methods perform as accurately as current state-of-the-art TSC algorhms, and th should rightfully be nsired further the TSC lerature.<br/></p><span></span><span></span><div class="Search_search-rult-provir__Qy9xz">Via<img alt="arxiv in" srcSet="/_next/image?;w=64&q=75 1x, /_next/image?;w=128&q=75 2x" src="/_next/image?;w=128&q=75" width="56" height="25" dg="async" data-nimg="1" class="Search_arxiv-in__h96mG" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-lk__iw6M7"><svg role="img" height="20" width="24" viewBox="0 0 24 24" xmlns=" style="marg-right:5px"><tle>Ghub In</tle><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 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data-ttid="paper-rult-tle" href="/paper/arxiv:2103.10247"><strong>Interpretable Feature Constctn for Time Seri Extrsic Regrsn</strong></a></h2><div class="Search_buttons-ntaer__q9Y_m"><a href="#" target="_blank" id="requt--2103.10247" data-ttid="view--button" class="Search_view--lk__Yn7"><button type="button" class="btn Search_view-button__hnDAt Search_buttons-spacg__R2x0Z Search_view--button__lScxO"><svg role="img" height="14" width="24" viewBox="0 0 24 24" xmlns=" fill="#fff"><tle>Ghub In</tle><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12"></path></svg>View Co</button></a><a class="Search_buttons-spacg__R2x0Z Search_add--button__xiBsy" target="_blank" href="/add_?tle=Interpretable Feature Constctn for Time Seri Extrsic Regrsn&><img alt="Add " tle="Contribute your for this paper to the muny" srcSet="/_next/image?;w=48&q=75 1x, /_next/image?;w=96&q=75 2x" src="/_next/image?;w=96&q=75" width="36" height="36" dg="async" data-nimg="1" loadg="lazy" style="lor:transparent"/></a><div class="wrapper Search_buttons-spacg__R2x0Z"><a tle="Get alerts when new is available for this paper"><img alt="Alert button" id="alert_btn" srcSet="/_next/image?;w=640&q=75 1x, /_next/image?;w=1080&q=75 2x" src="/_next/image?;w=1080&q=75" width="512" height="512" dg="async" data-nimg="1" class="alert-btn-image" loadg="lazy" style="lor:transparent"/></a><svg xmlns=" viewBox="0 0 106 34" style="marg-left:9px"><g class="sparkl"><path style="animatn:sparkle 2s 0s fe ease--out" d="M15.5740361 -10.33344622s1.1875777-6.20179466 2.24320232 0c0 0 5.9378885 1.05562462 0 2.11124925 0 0-1.05562463 6.33374774-2.24320233 0-3.5627331-.6597654-3.29882695-1.31953078 0-2.11124925z"></path><path style="animatn:sparkle 1.5s 0.9s fe ease--out" d="M33.5173993 75.97263826s1.03464615-5.40315215 1.95433162 0c0 0 5.17323078.91968547 0 1.83937095 0 0-.91968547 5.51811283-1.95433162 0-3.10393847-.57480342-2.8740171-1.14960684 0-1.83937095z"></path><path style="animatn:sparkle 1.7s 0.4s fe ease--out" d="M69.03038108 1.71240809s.73779281-3.852918 1.39360864 0c0 0 3.68896404.65581583 0 1.31163166 0 0-.65581583 3.93489497-1.39360864 0-2.21337842-.4098849-2.04942447-.81976979 0-1.31163166z"></path></g></svg></div></div><span class="Search_publitn-date__nSb6j">Mar 15, 2021<br/></span><span class="AuthorLks_thors-ntaer__r_iyO"><span><a data-ttid="paper-rult-thor" href="/thor/Domique%20Gay">Domique Gay</a>, </span><span><a data-ttid="paper-rult-thor" href="/thor/Alexis%20Bondu">Alexis Bondu</a>, </span><span><a data-ttid="paper-rult-thor" href="/thor/Vcent%20Lemaire">Vcent Lemaire</a>, </span><span><a data-ttid="paper-rult-thor" href="/thor/Marc%20Boull%C3%A9">Marc Boullé</a></span><br/><br/></span><div class="Search_paper-tail-page-imag-ntaer__Wphz5"><div class="Search_paper-imag__M3iM_"><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 1 for Interpretable Feature Constctn for Time Seri Extrsic Regrsn" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 2 for Interpretable Feature Constctn for Time Seri Extrsic Regrsn" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 3 for Interpretable Feature Constctn for Time Seri Extrsic Regrsn" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 4 for Interpretable Feature Constctn for Time Seri Extrsic Regrsn" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div></div></div><p class="Search_paper-ntent__eQ9jv">Supervised learng of time seri data has been extensively studied for the se of a tegoril target variable. In some applitn domas, e.g., energy, environment and health monorg, occurs that the target variable is numeril and the problem is known as time seri extrsic regrsn (TSER). In the lerature, some well-known time seri classifiers have been extend for TSER problems. As first benchmarkg studi have foced on predictive performance, very ltle attentn has been given to terpretabily. To fill this gap, this paper, we suggt an extensn of a Bayian method for robt and terpretable feature nstctn and selectn the ntext of TSER. Our approach explos a relatnal way to tackle wh TSER: (i), we build var and simple reprentatns of the time seri which are stored a relatnal data scheme, then, (ii), a proposnalisatn technique (based on classil aggregatn / selectn functns om the relatnal data field) is applied to build terpretable featur om sendary tabl to "flatten" the data; and (iii), the nstcted featur are filtered out through a Bayian Maximum A Posterri approach. The rultg transformed data n be procsed wh var existg regrsors. Experimental validatn on var benchmark data sets monstrat the benefs of the suggted approach.<br/></p><span></span><span></span><div class="Search_search-rult-provir__Qy9xz">Via<img alt="arxiv in" srcSet="/_next/image?;w=64&q=75 1x, /_next/image?;w=128&q=75 2x" src="/_next/image?;w=128&q=75" width="56" height="25" dg="async" data-nimg="1" class="Search_arxiv-in__h96mG" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-lk__iw6M7"><svg role="img" height="20" width="24" viewBox="0 0 24 24" xmlns=" style="marg-right:5px"><tle>Ghub In</tle><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 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</span><span><a data-ttid="paper-rult-thor" href="/thor/Domique%20Gay">Domique Gay</a></span><br/><br/></span><div class="Search_paper-tail-page-imag-ntaer__Wphz5"><div class="Search_paper-imag__M3iM_"><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 1 for Predictive K-means wh lol mols" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 2 for Predictive K-means wh lol mols" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 3 for Predictive K-means wh lol mols" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 4 for Predictive K-means wh lol mols" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div></div></div><p class="Search_paper-ntent__eQ9jv">Supervised classifitn n be effective for predictn but sometim weak on terpretabily or explaabily (XAI). Clterg, on the other hand, tends to isolate tegori or profil that n be meangful but there is no guarantee that they are eful for labels predictn. Predictive clterg seeks to obta the bt of the two worlds. Startg om labeled data, looks for clters that are as pure as possible wh regards to the class labels. One technique nsists tweakg a clterg algorhm so that data pots sharg the same label tend to aggregate together. Wh distance-based algorhms, such as k-means, a solutn is to modify the distance ed by the algorhm so that rporat rmatn about the labels of the data pots. In this paper, we propose another method which reli on a change of reprentatn guid by class nsi and then rri out clterg this new reprentatn space. We prent two new algorhms g this technique and show on a variety of data sets that they are petive for predictn performance wh pure supervised classifiers while offerg terpretabily of the clters disvered.<br/></p><span></span><span></span><div class="Search_search-rult-provir__Qy9xz">Via<img alt="arxiv in" srcSet="/_next/image?;w=64&q=75 1x, /_next/image?;w=128&q=75 2x" src="/_next/image?;w=128&q=75" width="56" height="25" dg="async" data-nimg="1" class="Search_arxiv-in__h96mG" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-lk__iw6M7"><svg role="img" height="20" width="24" viewBox="0 0 24 24" xmlns=" style="marg-right:5px"><tle>Ghub In

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