Help
RSS
API
Feed
Maltego
Contact
Domain > tseregression.org
×
More information on this domain is in
AlienVault OTX
Is this malicious?
Yes
No
DNS Resolutions
Date
IP Address
2025-09-22
3.5.165.63
(
ClassC
)
2025-09-22
3.5.169.193
(
ClassC
)
2025-09-28
3.5.169.36
(
ClassC
)
2025-11-17
52.95.128.141
(
ClassC
)
Port 80
HTTP/1.1 200 OKx-amz-id-2: X6LQa+Hfh2xWPgNOGaMgTnqFfTg2uEwA50lun1GcqJoQ+0g3cAj9mdc14r9cknPSdrNtkZqXFDYx-amz-request-id: M1VSZBZN54DPR52RDate: Mon, 17 Nov 2025 18:51:03 GMTLast-Modified: Wed, 24 Mar 2021 04:03:49 GMTx-amz-version-id: mLkX4ZBB_uuBE1Tvcau3zk20FMvQj2FkETag: d6392a7ccd1d3a14657e5871569c4343Content-Type: text/htmlContent-Length: 39598Server: AmazonS3 !DOCTYPE html>html langen>head> meta charsetutf-8> meta nameviewport contentwidthdevice-width, initial-scale1, shrink-to-fitno> meta namedescription content> meta nameauthor content> title>Time Series Extrinsic Regression/title> !-- Favicons --> link relicon typeimage/png sizes32x32 hreffigures/favicon.png> link relmanifest href/site.webmanifest> !-- Bootstrap core CSS --> link hrefvendor/bootstrap/css/bootstrap.min.css relstylesheet> !-- Custom styles for this template --> link hrefcss/scrolling-nav.css relstylesheet> link relstylesheet hrefhttps://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css integritysha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T crossoriginanonymous> link relstylesheet hrefhttps://use.fontawesome.com/releases/v5.6.3/css/all.css integritysha384-UHRtZLI+pbxtHCWp1t77Bi1L4ZtiqrqD80Kn4Z8NTSRyMA2Fd33n5dQ8lWUE00s/ crossoriginanonymous> link relstylesheet hrefhttps://unpkg.com/bootstrap-table@1.16.0/dist/bootstrap-table.min.css> !-- Google Fonts --> link hrefhttps://fonts.googleapis.com/css?familyOpen+Sans:300,300i,400,400i,600,600i,700,700i|Raleway:300,300i,400,400i,500,500i,600,600i,700,700i|Poppins:300,300i,400,400i,500,500i,600,600i,700,700i relstylesheet> link hrefvendor/boxicons/css/boxicons.min.css relstylesheet> !-- Fontawesome --> script srchttps://kit.fontawesome.com/a0ae9c6c76.js crossoriginanonymous>/script> script srchttps://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js>/script> script srchttps://maxcdn.bootstrapcdn.com/bootstrap/3.4.1/js/bootstrap.min.js>/script> /head>body idpage-top> !-- Navigation --> nav classnavbar navbar-expand-lg navbar-dark bg-dark fixed-top idmainNav> div classcontainer> a classnavbar-brand js-scroll-trigger href#page-top>Time Series Extrinsic Regression/a> button classnavbar-toggler typebutton data-togglecollapse data-target#navbarResponsive aria-controlsnavbarResponsive aria-expandedfalse aria-labelToggle navigation> span classnavbar-toggler-icon>/span> /button> div classcollapse navbar-collapse idnavbarResponsive> ul classnavbar-nav ml-auto> li classnav-item> a classnav-link js-scroll-trigger href#datasets>Datasets/a> /li> li classnav-item> a classnav-link js-scroll-trigger href#results>Results/a> /li> li classnav-item> a classnav-link js-scroll-trigger href#publications>Publications/a> /li> li classnav-item> a classnav-link js-scroll-trigger href#aboutus>About Us/a> /li> li classnav-item> a classnav-link js-scroll-trigger hrefhttps://github.com/ChangWeiTan/TS-Extrinsic-Regression target_blank>Software/a> /li> /ul> /div> /div> /nav> header> div classcontainer text-center> h1>Welcome to the Monash, UEA & UCR br/> Time Series Extrinsic Regression Repository/h1> img srcfigures/logo/Monash.png classimg img-responsive width150 altmonash> img srcfigures/logo/UEA.png classimg img-responsive width150 altuea> img srcfigures/logo/UCR.png classimg img-responsive width150 altucr> /div> /header> div classcontainer> div classrow> div classcol-lg> p classlead> This website aims to support research into strong>Time Series Extrinsic Regression (TSER)/strong>, em>a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable/em> a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label. We recommend you to read the a hrefhttps://arxiv.org/abs/2006.10996 target_blank>paper/a> for a detailed discussion of the datasets and their sources. If you use the results or code, please cite the paper Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I. Webb, strong>Time Series Extrinsic Regression: Predicting numeric values from time series data/strong>./p> pre> @article{ Tan2020TSER, title{Time Series Extrinsic Regression}, author{Tan, Chang Wei and Bergmeir, Christoph and Petitjean, Francois and Webb, Geoffrey I}, journal{Data Mining and Knowledge Discovery}, pages{1--29}, year{2021}, publisher{Springer}, doi{https://doi.org/10.1007/s10618-021-00745-9} } /pre> p classlead>If you just use the website, please reference the website as:/p> Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Daniel Schmidt, Geoffrey I. Webb, Anthony Bagnall, & Eamonn Keogh (2020). The Monash, UEA & UCR Time Series Extrinsic Regression Archive. URL http://tseregression.org/. /div> div classcol-sm> h2 classpage-header>center>News/center>/h2> table classtable> tbody> tr> td>March 2021/td> td>Updated the LiveFuelMoistureContent dataset./td> /tr> tr> td>March 2021/td> td>The paper Time Series Extrinsic Regression is now published in a hrefhttps://link.springer.com/article/10.1007/s10618-021-00745-9 target_blank>Data Mining and Knowledge Discovery/a>./td> /tr> tr> td>December 2020/td> td>The paper Time Series Extrinsic Regression is now in Press./td> /tr> /tbody> /table> /div> /div> /div> section iddatasets classbg-light> div classcontainer> div classrow> div classmx-auto> h2>center>Datasets/center>/h2> p classlead>The following table shows a list of time series extrinsic regression datasets. You can download the entire spreadsheet displayed below a hrefhttp://tseregression.org/data/ts_regression.xlsx target_blank>here/a> and the whole dataset a hrefhttps://zenodo.org/record/3902651 target_blank>here/a> (about 600 MB). The datasets are available in sktime a hrefhttps://alan-turing-institute.github.io/sktime/examples/loading_data.html target_blank>.ts/a> format. An example of loading the data can be found in our a hrefhttps://github.com/ChangWeiTan/TS-Extrinsic-Regression/blob/master/utils/data_loader.py target_blank>github repository/a>./p> p classlead>The a href#results>results page/a> shows some baseline on these data using typical regressors./p> table iddatasetTable data-toggletable classsortable table-hover cellspacing0 width100%> thead> th classth-sm data-sortabletrue>ID/th> th classth-sm data-sortabletrue>Type/th> th classth-sm data-sortabletrue>Dataset/th> th classth-sm data-sortabletrue>Train Size/th> th classth-sm data-sortabletrue>Test Size/th> th classth-sm data-sortabletrue>Length/th> th classth-sm data-sortabletrue>Dimension/th> th classth-sm data-sortabletrue>Missing Values/th> th classth-sm data-sortabletrue>Donor/Source/th> /thead> tbody> tr> td>1/td> td>Energy Monitoring/td> td>a hrefhttps://zenodo.org/record/3902637 target_blank>AppliancesEnergy/a>/td> td>96/td> td>42/td> td>144/td> td>24/td> td>No/td> td>Luis Candanedo (UCI Repository)/td> /tr> tr> td>2/td> td>Energy Monitoring/td> td>a hrefhttps://zenodo.org/record/3902704 target_blank>HouseholdPowerConsumption1/a>/td> td>746/td> td>694/td> td>1440/td> td>5/td> td>Yes/td> td>Georges Hebrail & Alice Berard (UCI Repository)/td> /tr> tr> td>3/td> td>Energy Monitoring/td> td>a hrefhttps://zenodo.org/record/3902706 target_blank>HouseholdPowerConsumption2/a>/td> td>746/td> td>694/td> td>1440/td> td>5/td> td>Yes/td> td>Georges Hebrail & Alice Berard (UCI Repository)/td> /tr> tr> td>4/td> td>Environment Monitoring/td> td>a hrefhttps://zenodo.org/record/3902673 target_blank>BenzeneConcentration/a>/td> td>3433/td> td>5445/td> td>240/td> td>8/td> td>Yes/td> td>Saverio De Vito (UCI Repository)/td> /tr> tr> td>5/td> td>Environment Monitoring/td> td>a hrefhttps://zenodo.org/record/3902671 target_blank>BeijingPM25Quality/a>/td> td>12432/td> td>5100/td> td>24/td> td>9/td> td>Yes/td> td>Song Xi Chen (UCI Repository)/td> /tr> tr> td>6/td> td>Environment Monitoring/td> td>a hrefhttps://zenodo.org/record/3902667 target_blank>BeijingPM10Quality/a>/td> td>12432/td> td>5100/td> td>24/td> td>9/td> td>Yes/td> td>Song Xi Chen (UCI Repository)/td> /tr> tr> td>7/td> td>Environment Monitoring/td> td>a hrefhttps://zenodo.org/record/3902716 target_blank>LiveFuelMoistureContent/a>/td> td>3493/td> td>1510/td> td>365/td> td>7/td> td>No/td> td>LiuJun Zhu/td> /tr> tr> td>8/td> td>Environment Monitoring/td> td>a hrefhttps://zenodo.org/record/3902694 target_blank>FloodModeling1/a>/td> td>471/td> td>202/td> td>266/td> td>1/td> td>No/td> td>Jihane Elyahyioui/td> /tr> tr> td>9/td> td>Environment Monitoring/td> td>a hrefhttps://zenodo.org/record/3902696 target_blank>FloodModeling2/a>/td> td>389/td> td>167/td> td>266/td> td>1/td> td>No/td> td>Jihane Elyahyioui/td> /tr> tr> td>10/td> td>Environment Monitoring/td> td>a hrefhttps://zenodo.org/record/3902698 target_blank>FloodModeling3/a>/td> td>429/td> td>184/td> td>266/td> td>1/td> td>No/td> td>Jihane Elyahyioui/td> /tr> tr> td>11/td> td>Environment Monitoring/td> td>a hrefhttps://zenodo.org/record/3902654 target_blank>AustraliaRainfall/a>/td> td>112186/td> td>48081/td> td>24/td> td>3/td> td>No/td> td>Bureau of Meteorology Australia/td> /tr> tr> td>12/td> td>Health Monitoring/td> td>a hrefhttps://zenodo.org/record/3902728 target_blank>PPGDalia/a>/td> td>43215/td> td>21482/td> td>256-512*/td> td>4/td> td>No/td> td>Attila Reiss, Ina Indlekofer & Philip Schmidt (UCI Repository)/td> /tr> tr> td>13/td> td>Health Monitoring/td> td>a hrefhttps://zenodo.org/record/3902710 target_blank>IEEEPPG/a>/td> td>1768/td> td>1328/td> td>1000/td> td>5/td> td>No/td> td>Zhilin Zhang (IEEE Signal Processing Cup 2015)/td> /tr> tr> td>14/td> td>Health Monitoring/td> td>a hrefhttps://zenodo.org/record/3902685 target_blank>BIDMCRR/a>/td> td>5471/td> td>2399/td> td>4000/td> td>2/td> td>No/td> td>Peter Charlton & Marco Pimentel (PhysioNet)/td> /tr> tr> td>15/td> td>Health Monitoring/td> td>a hrefhttps://zenodo.org/record/3902676 target_blank>BIDMCHR/a>/td> td>5550/td> td>2399/td> td>4000/td> td>2/td> td>No/td> td>Peter Charlton & Marco Pimentel (PhysioNet)/td> /tr> tr> td>16/td> td>Health Monitoring/td> td>a hrefhttps://zenodo.org/record/3902688 target_blank>BIDMCSpO2/a>/td> td>5550/td> td>2399/td> td>4000/td> td>2/td> td>No/td> td>Peter Charlton & Marco Pimentel (PhysioNet)/td> /tr> tr> td>17/td> td>Sentiment Analysis/td> td>a hrefhttps://zenodo.org/record/3902718 target_blank>NewsHeadlineSentiment/a>/td> td>58213/td> td>24951/td> td>144/td> td>3/td> td>No/td> td>Nuno Moniz & LuÃs Torgo (UCI Repository)/td> /tr> tr> td>18/td> td>Sentiment Analysis/td> td>a hrefhttps://zenodo.org/record/3902726 target_blank>NewsTitleSentiment/a>/td> td>58213/td> td>24951/td> td>144/td> td>3/td> td>No/td> td>Nuno Moniz & LuÃs Torgo (UCI Repository)/td> /tr> tr> td>19/td> td>Forecasting/td> td>a hrefhttps://zenodo.org/record/3902690 target_blank>Covid3Month/a>/td> td>140/td> td>61/td> td>84/td> td>1/td> td>No/td> td>covid19.who.int/td> /tr> /tbody> /table> script srchttps://code.jquery.com/jquery-3.3.1.min.js integritysha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8 crossoriginanonymous>/script> script srchttps://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js integritysha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1 crossoriginanonymous>/script> script srchttps://stackpath.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js integritysha384-JjSmVgyd0p3pXB1rRibZUAYoIIy6OrQ6VrjIEaFf/nJGzIxFDsf4x0xIM+B07jRM crossoriginanonymous>/script> script srchttps://unpkg.com/bootstrap-table@1.16.0/dist/bootstrap-table.min.js>/script> p classlead>* These datasets have equal length series but differs between dimensions./p> /div> /div> /div> /section> section idresults> div classcontainer> div classrow> div classmx-auto> h2>center>Results/center>/h2> div classcontainer text-center> img srcfigures/ts_regression_cd.png width1000 altcd> figcaption>Critical difference diagram for 13 algorithms. Solid bar means that there is no significant difference in rank between methods (see a hrefhttps://www.jmlr.org/papers/volume7/demsar06a/demsar06a.pdf>Demsar 2006/a>). Tests are performed with the two-tailed Nemenyi test./figcaption> /div> /div> /div> /div> br> div classcontainer> p classlead>The following is the raw result for each of the dataset and regressor. The same results can be obtained from a hrefhttp://tseregression.org/data/ts_regression.xlsx target_blank>here/a>/p> table idresultsTable data-toggletable classsortable table-hover cellspacing0 width100%> thead> th classth-sm data-sortabletrue>Dataset Name/th> th classth-sm data-sortabletrue>FPCR/th> th classth-sm data-sortabletrue>FPCR-Bspline/th> th classth-sm data-sortabletrue>SVR Optimised/th> th classth-sm data-sortabletrue>RandomForest/th> th classth-sm data-sortabletrue>XGBoost/th> th classth-sm data-sortabletrue>1-NN-ED/th> th classth-sm data-sortabletrue>5-NN-ED/th> th classth-sm data-sortabletrue>1-NN-DTWD/th> th classth-sm data-sortabletrue>5-NN-DTWD/th> th classth-sm data-sortabletrue>Rocket/th> th classth-sm data-sortabletrue>FCN/th> th classth-sm data-sortabletrue>ResNet/th> th classth-sm data-sortabletrue>InceptionNetwork/th> /thead> tbody> tr classrow1> td classcolumn0 style0 s>AppliancesEnergy/td> td classcolumn1 style0 n>5.405052/td> td classcolumn2 style0 n>5.405052/td> td classcolumn3 style0 n>3.454574/td> td classcolumn4 style0 n>3.4551198/td> td classcolumn5 style0 n>3.489024/td> td classcolumn6 style0 n>5.231953/td> td classcolumn7 style0 n>4.227438/td> td classcolumn8 style0 n>6.036547/td> td classcolumn9 style0 n>4.019873/td> td classcolumn10 style0 n>2.2990312/td> td classcolumn11 style0 n>2.865684/td> td classcolumn12 style0 n>3.065047/td> td classcolumn13 style0 n>4.43533/td> /tr> tr classrow2> td classcolumn0 style0 s>AustraliaRainfall/td> td classcolumn1 style0 n>8.436335/td> td classcolumn2 style0 n>8.436336/td> td classcolumn3 style0 n>8.650856/td> td classcolumn4 style0 n>8.389541/td> td classcolumn5 style0 n>8.492986/td> td classcolumn6 style0 n>30.254139/td> td classcolumn7 style0 n>10.232841/td> td classcolumn8 style0 n>12.001981/td> td classcolumn9 style0 n>11.95073/td> td classcolumn10 style0 n>8.124137333/td> td classcolumn11 style0 n>8.425874/td> td classcolumn12 style0 n>8.179173/td> td classcolumn13 style0 n>8.841251/td> /tr> tr classrow3> td classcolumn0 style0 s>BeijingPM10Quality/td> td classcolumn1 style0 n>99.725946/td> td classcolumn2 style0 n>99.732125/td> td classcolumn3 style0 n>110.574226/td> td classcolumn4 style0 n>94.072344/td> td classcolumn5 style0 n>93.138127/td> td classcolumn6 style0 n>139.22979/td> td classcolumn7 style0 n>115.669411/td> td classcolumn8 style0 n>139.134908/td> td classcolumn9 style0 n>115.502744/td> td classcolumn10 style0 n>120.0577646/td> td classcolumn11 style0 n>94.348729/td> td classcolumn12 style0 n>95.489374/td> td classcolumn13 style0 n>96.749997/td> /tr> tr classrow4> td classcolumn0 style0 s>BeijingPM25Quality/td> td classcolumn1 style0 n>69.379217/td> td classcolumn2 style0 n>69.369892/td> td classcolumn3 style0 n>71.437076/td> td classcolumn4 style0 n>63.301428/td> td classcolumn5 style0 n>59.495865/td> td classcolumn6 style0 n>88.193545/td> td classcolumn7 style0 n>74.156382/td> td classcolumn8 style0 n>88.256082/td> td classcolumn9 style0 n>72.717689/td> td classcolumn10 style0 n>62.769655/td> td classcolumn11 style0 n>59.726847/td> td classcolumn12 style0 n>64.462746/td> td classcolumn13 style0 n>62.227924/td> /tr> tr classrow5> td classcolumn0 style0 s>BenzeneConcentration/td> td classcolumn1 style0 n>11.088396/td> td classcolumn2 style0 n>11.094974/td> td classcolumn3 style0 n>4.790901/td> td classcolumn4 style0 n>0.855559/td> td classcolumn5 style0 n>0.6377256/td> td classcolumn6 style0 n>6.535685/td> td classcolumn7 style0 n>5.84498/td> td classcolumn8 style0 n>4.983578/td> td classcolumn9 style0 n>4.868465/td> td classcolumn10 style0 n>3.360614/td> td classcolumn11 style0 n>4.988295/td> td classcolumn12 style0 n>4.0612608/td> td classcolumn13 style0 n>1.584852/td> /tr> tr classrow6> td classcolumn0 style0 s>BIDMC32HR/td> td classcolumn1 style0 n>13.980558/td> td classcolumn2 style0 n>13.980597/td> td classcolumn3 style0 n>13.39297/td> td classcolumn4 style0 n>15.016468/td> td classcolumn5 style0 n>13.963799/td> td classcolumn6 style0 n>14.836506/td> td classcolumn7 style0 n>14.756088/td> td classcolumn8 style0 n>15.29101/td> td classcolumn9 style0 n>15.127008/td> td classcolumn10 style0 n>13.9443828/td> td classcolumn11 style0 n>13.130665/td> td classcolumn12 style0 n>10.74142/td> td classcolumn13 style0 n>9.424679/td> /tr> tr classrow7> td classcolumn0 style0 s>BIDMC32RR/td> td classcolumn1 style0 n>3.364777/td> td classcolumn2 style0 n>3.364704/td> td classcolumn3 style0 n>3.17366/td> td classcolumn4 style0 n>4.350314/td> td classcolumn5 style0 n>4.367828/td> td classcolumn6 style0 n>4.387345/td> td classcolumn7 style0 n>4.134685/td> td classcolumn8 style0 n>3.529111/td> td classcolumn9 style0 n>3.432247/td> td classcolumn10 style0 n>4.0929006/td> td classcolumn11 style0 n>3.577775/td> td classcolumn12 style0 n>3.921214/td> td classcolumn13 style0 n>3.018405/td> /tr> tr classrow8> td classcolumn0 style0 s>BIDMC32SpO2/td> td classcolumn1 style0 n>4.953519/td> td classcolumn2 style0 n>4.953517/td> td classcolumn3 style0 n>4.796855/td> td classcolumn4 style0 n>4.570262/td> td classcolumn5 style0 n>4.450805/td> td classcolumn6 style0 n>5.530202/td> td classcolumn7 style0 n>5.407875/td> td classcolumn8 style0 n>5.215027/td> td classcolumn9 style0 n>5.123964/td> td classcolumn10 style0 n>5.221737/td> td classcolumn11 style0 n>5.968337/td> td classcolumn12 style0 n>5.987832/td> td classcolumn13 style0 n>5.57612/td> /tr> tr classrow9> td classcolumn0 style0 s>Covid3Month/td> td classcolumn1 style0 n>0.044912/td> td classcolumn2 style0 n>0.044912/td> td classcolumn3 style0 n>0.06584/td> td classcolumn4 style0 n>0.0424/td> td classcolumn5 style0 n>0.044682/td> td classcolumn6 style0 n>0.05306/td> td classcolumn7 style0 n>0.041815/td> td classcolumn8 style0 n>0.052735/td> td classcolumn9 style0 n>0.042943/td> td classcolumn10 style0 n>0.0438782/td> td classcolumn11 style0 n>0.07434/td> td classcolumn12 style0 n>0.095338/td> td classcolumn13 style0 n>0.053769/td> /tr> tr classrow10> td classcolumn0 style0 s>FloodModeling1/td> td classcolumn1 style0 n>0.018853/td> td classcolumn2 style0 n>0.018853/td> td classcolumn3 style0 n>0.046304/td> td classcolumn4 style0 n>0.015891/td> td classcolumn5 style0 n>0.0159712/td> td classcolumn6 style0 n>0.01482/td> td classcolumn7 style0 n>0.016193/td> td classcolumn8 style0 n>0.011689/td> td classcolumn9 style0 n>0.009801/td> td classcolumn10 style0 n>0.002356/td> td classcolumn11 style0 n>0.006709/td> td classcolumn12 style0 n>0.008868/td> td classcolumn13 style0 n>0.01743/td> /tr> tr classrow11> td classcolumn0 style0 s>FloodModeling2/td> td classcolumn1 style0 n>0.019079/td> td classcolumn2 style0 n>0.019079/td> td classcolumn3 style0 n>0.075804/td> td classcolumn4 style0 n>0.014095/td> td classcolumn5 style0 n>0.018199/td> td classcolumn6 style0 n>0.018552/td> td classcolumn7 style0 n>0.018586/td> td classcolumn8 style0 n>0.016356/td> td classcolumn9 style0 n>0.016238/td> td classcolumn10 style0 n>0.005881/td> td classcolumn11 style0 n>0.006719/td> td classcolumn12 style0 n>0.013939/td> td classcolumn13 style0 n>0.00729/td> /tr> tr classrow12> td classcolumn0 style0 s>FloodModeling3/td> td classcolumn1 style0 n>0.021458/td> td classcolumn2 style0 n>0.021458/td> td classcolumn3 style0 n>0.035032/td> td classcolumn4 style0 n>0.020429/td> td classcolumn5 style0 n>0.0207038/td> td classcolumn6 style0 n>0.019947/td> td classcolumn7 style0 n>0.020765/td> td classcolumn8 style0 n>0.01375/td> td classcolumn9 style0 n>0.013337/td> td classcolumn10 style0 n>0.004064/td> td classcolumn11 style0 n>0.007873/td> td classcolumn12 style0 n>0.01558/td> td classcolumn13 style0 n>0.00821/td> /tr> tr classrow13> td classcolumn0 style0 s>HouseholdPowerConsumption1/td> td classcolumn1 style0 n>147.548998/td> td classcolumn2 style0 n>147.5492/td> td classcolumn3 style0 n>152.391358/td> td classcolumn4 style0 n>248.858964/td> td classcolumn5 style0 n>231.089829/td> td classcolumn6 style0 n>473.932736/td> td classcolumn7 style0 n>432.594707/td> td classcolumn8 style0 n>427.04311/td> td classcolumn9 style0 n>297.221675/td> td classcolumn10 style0 n>132.798779/td> td classcolumn11 style0 n>162.244492/td> td classcolumn12 style0 n>193.207281/td> td classcolumn13 style0 n>153.716402/td> /tr> tr classrow14> td classcolumn0 style0 s>HouseholdPowerConsumption2/td> td classcolumn1 style0 n>46.925185/td> td classcolumn2 style0 n>46.929783/td> td classcolumn3 style0 n>55.98083/td> td classcolumn4 style0 n>46.932139/td> td classcolumn5 style0 n>44.3729326/td> td classcolumn6 style0 n>71.479369/td> td classcolumn7 style0 n>64.272956/td> td classcolumn8 style0 n>58.802634/td> td classcolumn9 style0 n>51.494969/td> td classcolumn10 style0 n>32.607104/td> td classcolumn11 style0 n>46.829256/td> td classcolumn12 style0 n>39.080121/td> td classcolumn13 style0 n>39.409826/td> /tr> tr classrow15> td classcolumn0 style0 s>IEEEPPG/td> td classcolumn1 style0 n>31.381214/td> td classcolumn2 style0 n>31.381212/td> td classcolumn3 style0 n>37.254146/td> td classcolumn4 style0 n>32.10907/td> td classcolumn5 style0 n>31.487901/td> td classcolumn6 style0 n>33.208862/td> td classcolumn7 style0 n>27.111213/td> td classcolumn8 style0 n>37.140393/td> td classcolumn9 style0 n>33.572786/td> td classcolumn10 style0 n>36.5154892/td> td classcolumn11 style0 n>34.325728/td> td classcolumn12 style0 n>33.150985/td> td classcolumn13 style0 n>23.903929/td> /tr> tr classrow16> td classcolumn0 style0 s>LFMC/td> td classcolumn1 style0 n>37.683857/td> td classcolumn2 style0 n>37.688074/td> td classcolumn3 style0 n>39.733527/td> td classcolumn4 style0 n>32.1626252/td> td classcolumn5 style0 n>32.441886/td> td classcolumn6 style0 n>47.836798/td> td classcolumn7 style0 n>38.535526/td> td classcolumn8 style0 n>39.971707/td> td classcolumn9 style0 n>35.185301/td> td classcolumn10 style0 n>29.4097538/td> td classcolumn11 style0 n>33.25722/td> td classcolumn12 style0 n>30.3516564/td> td classcolumn13 style0 n>28.796294/td> /tr> tr classrow17> td classcolumn0 style0 s>NewsHeadlineSentiment/td> td classcolumn1 style0 n>0.142273/td> td classcolumn2 style0 n>0.142272/td> td classcolumn3 style0 n>0.142917/td> td classcolumn4 style0 n>0.147582/td> td classcolumn5 style0 n>0.142486/td> td classcolumn6 style0 n>0.202821/td> td classcolumn7 style0 n>0.156636/td> td classcolumn8 style0 n>0.197937/td> td classcolumn9 style0 n>0.155839/td> td classcolumn10 style0 n>0.142244/td> td classcolumn11 style0 n>0.148065/td> td classcolumn12 style0 n>0.150024/td> td classcolumn13 style0 n>0.150014/td> /tr> tr classrow18> td classcolumn0 style0 s>NewsTitleSentiment/td> td classcolumn1 style0 n>0.138126/td> td classcolumn2 style0 n>0.138126/td> td classcolumn3 style0 n>0.138881/td> td classcolumn4 style0 n>0.143103/td> td classcolumn5 style0 n>0.138336/td> td classcolumn6 style0 n>0.193318/td> td classcolumn7 style0 n>0.15095/td> td classcolumn8 style0 n>0.187257/td> td classcolumn9 style0 n>0.150564/td> td classcolumn10 style0 n>0.138059/td> td classcolumn11 style0 n>0.138082/td> td classcolumn12 style0 n>0.138295/td> td classcolumn13 style0 n>0.158558/td> /tr> tr classrow19> td classcolumn0 style0 s>PPGDalia/td> td classcolumn1 style0 n>20.674488/td> td classcolumn2 style0 n>20.674486/td> td classcolumn3 style0 n>19.005216/td> td classcolumn4 style0 n>17.530628/td> td classcolumn5 style0 n>16.58273/td> td classcolumn6 style0 n>21.876567/td> td classcolumn7 style0 n>18.282277/td> td classcolumn8 style0 n>26.024576/td> td classcolumn9 style0 n>20.768389/td> td classcolumn10 style0 n>14.050544/td> td classcolumn11 style0 n>13.038805/td> td classcolumn12 style0 n>11.382165/td> td classcolumn13 style0 n>9.923701/td> /tr> /tbody> /table> /div> /section> section idpublications> div classcontainer> div classrow> div classmx-auto> h2>center>Publications/center>/h2> !-- h3>i classbx bx-loader bx-spin>/i>Working papers/h3> div classrow> ol> li> C. Tan, C. Bergmeir, F. Petitjean, and G. Webb, b>Monash University, UEA, UCR Time Series Extrinsic Regression Archive/b> a hrefhttps://arxiv.org/pdf/2006.10996.pdf target_blank>i classfa fa-file-pdf-o>/i>/a> /li> li> C. Tan, C. Bergmeir, F. Petitjean, and G. Webb, b>Time Series Extrinsic Regression/b> a hrefhttps://arxiv.org/pdf/2006.12672.pdf target_blank>i classfa fa-file-pdf-o>/i>/a> /li> /ol> /div> --> ol> li> C. Tan, C. Bergmeir, F. Petitjean, and G. Webb, b>Time Series Extrinsic Regression: Predicting numeric values from time series data/b> in i>Data Mining and Knowledge Discovery 2021/i> a hrefhttps://doi.org/10.1007/s10618-021-00745-9 target_blank>doi/a> a hrefhttps://doi.org/10.1007/s10618-021-00745-9 target_blank>i classfa fa-file-pdf-o>/i>/a> /li> li> C. Tan, C. Bergmeir, F. Petitjean, and G. Webb, b>Monash University, UEA, UCR Time Series Extrinsic Regression Archive/b> a hrefhttps://arxiv.org/pdf/2006.10996.pdf target_blank>i classfa fa-file-pdf-o>/i>/a> /li> /ol> /div> /div> /div> /section> section idaboutus classbg-light> div classcontainer> div classrow> div classmx-auto> h2>center>About Us/center>/h2> p classlead>We are a group of time series researchers from Monash University, University of East Anglia and University of California Riverside:/p> table idtable cellspacing0 width100% classcenter> tr> td> a hrefhttps://changweitan.com/ target_blank>img srcfigures/changwei.jpg classimg img-fluid rounded-circle width150 altchangwei>figcaption classtext-center>Chang Wei Tan/figcaption>/a> /td> td> a hrefhttps://www.uea.ac.uk/computing/people/profile/anthony-bagnall target_blank>img srcfigures/tony.jpg classimg img-fluid rounded-circle width150 alttony>figcaption classtext-center>Anthony Bagnall/figcaption>/a> /td> td> a hrefhttps://research.monash.edu/en/persons/christoph-bergmeir target_blank>img srcfigures/christoph.jpg classimg img-fluid rounded-circle width150 altchristoph>figcaption classtext-center>Christoph Bergmeir/figcaption>/a> /td> td> a hrefhttps://research.monash.edu/en/persons/daniel-schmidt target_blank>img srcfigures/daniel.png classimg img-fluid rounded-circle width150 altdaniel>figcaption classtext-center>Daniel Schmidt/figcaption>/a> /td> td> a hrefhttp://www.cs.ucr.edu/~eamonn/ target_blank>img srcfigures/eamonn.jpg classimg img-fluid rounded-circle width150 alteamonn>figcaption classtext-center>Eamonn Keogh/figcaption>/a> /td> td> a hrefhttps://www.francois-petitjean.com/ target_blank>img srcfigures/francois.jpg classimg img-responsive rounded-circle width150 altfrancois>figcaption classtext-center>François Petitjean/figcaption>/a> /td> td> a hrefhttp://i.giwebb.com/ target_blank>img srcfigures/geoff.jpg classimg img-responsive rounded-circle width150 altgeoff>figcaption classtext-center>Geoff Webb/figcaption>/a> /td> /tr> /table> /div> /div> /div> /section> !-- Footer --> footer classpy-5 bg-dark> div classcontainer> p classm-0 text-center text-white>Copyright © Time Series Regression 2021/p> /div> !-- /.container --> /footer> !-- Bootstrap core JavaScript --> script srcvendor/jquery/jquery.min.js>/script> script srcvendor/bootstrap/js/bootstrap.bundle.min.js>/script> !-- Plugin JavaScript --> script srcvendor/jquery-easing/jquery.easing.min.js>/script> !-- Custom JavaScript for this theme --> script srcjs/scrolling-nav.js>/script>/body>/html>
View on OTX
|
View on ThreatMiner
Please enable JavaScript to view the
comments powered by Disqus.
Data with thanks to
AlienVault OTX
,
VirusTotal
,
Malwr
and
others
. [
Sitemap
]