-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathchunk-5989ee3a.c083a877.js
2 lines (2 loc) · 22.7 KB
/
chunk-5989ee3a.c083a877.js
1
2
(window["webpackJsonp"]=window["webpackJsonp"]||[]).push([["chunk-5989ee3a"],{"000c":function(e,t,m){"use strict";var _=m("808f"),s=m("a368");e.exports=Object.keys||function(e){return _(e,s)}},"0e15":function(e,t,m){"use strict";var _=m("72a9"),s=m("6eab"),p=m("90ff"),a=m("2b31"),r=m("bc5b"),n=m("000c");t.f=_&&!s?Object.defineProperties:function(e,t){a(e);var m,_=r(t),s=n(t),o=s.length,i=0;while(o>i)p.f(e,m=s[i++],_[m]);return e}},"3f7a":function(e,t,m){"use strict";m("9d39")},"484e":function(e,t,m){"use strict";var _=m("8922"),s=m("2248"),p=m("4ae8"),a=m("2b31"),r=m("6403"),n=m("f0dc"),o=m("84ce"),i=m("9134"),c=m("d3d6"),u=m("140c"),f=m("1794"),l=m("ffe2").IteratorPrototype,d=m("72a9"),h=m("6bcb"),v="constructor",k="Iterator",b=f("toStringTag"),w=TypeError,y=s[k],g=h||!r(y)||y.prototype!==l||!c((function(){y({})})),x=function(){if(p(this,l),n(this)===l)throw new w("Abstract class Iterator not directly constructable")},E=function(e,t){d?o(l,e,{configurable:!0,get:function(){return t},set:function(t){if(a(this),this===l)throw new w("You can't redefine this property");u(this,e)?this[e]=t:i(this,e,t)}}):l[e]=t};u(l,b)||E(b,k),!g&&u(l,v)&&l[v]!==Object||E(v,x),x.prototype=l,_({global:!0,constructor:!0,forced:g},{Iterator:x})},"4ae8":function(e,t,m){"use strict";var _=m("eae2"),s=TypeError;e.exports=function(e,t){if(_(t,e))return e;throw new s("Incorrect invocation")}},"5d32":function(e,t,m){"use strict";var _=m("d3d6");e.exports=!_((function(){function e(){}return e.prototype.constructor=null,Object.getPrototypeOf(new e)!==e.prototype}))},"71f6":function(e,t,m){"use strict";var _=m("74aa"),s=m("19c1");e.exports=function(e){if("Function"===_(e))return s(e)}},"83b8":function(e,t,m){"use strict";var _=m("bcbd"),s=m("9918"),p=m("3be3"),a=m("dc72"),r=m("1794"),n=r("iterator");e.exports=function(e){if(!p(e))return s(e,n)||s(e,"@@iterator")||a[_(e)]}},"84ce":function(e,t,m){"use strict";var _=m("69bd"),s=m("90ff");e.exports=function(e,t,m){return m.get&&_(m.get,t,{getter:!0}),m.set&&_(m.set,t,{setter:!0}),s.f(e,t,m)}},"8bd3":function(e,t,m){"use strict";var _,s=m("2b31"),p=m("0e15"),a=m("a368"),r=m("3c5c"),n=m("b3ba"),o=m("88f1"),i=m("379e"),c=">",u="<",f="prototype",l="script",d=i("IE_PROTO"),h=function(){},v=function(e){return u+l+c+e+u+"/"+l+c},k=function(e){e.write(v("")),e.close();var t=e.parentWindow.Object;return e=null,t},b=function(){var e,t=o("iframe"),m="java"+l+":";return t.style.display="none",n.appendChild(t),t.src=String(m),e=t.contentWindow.document,e.open(),e.write(v("document.F=Object")),e.close(),e.F},w=function(){try{_=new ActiveXObject("htmlfile")}catch(t){}w="undefined"!=typeof document?document.domain&&_?k(_):b():k(_);var e=a.length;while(e--)delete w[f][a[e]];return w()};r[d]=!0,e.exports=Object.create||function(e,t){var m;return null!==e?(h[f]=s(e),m=new h,h[f]=null,m[d]=e):m=w(),void 0===t?m:p.f(m,t)}},"8f5e":function(e,t,m){"use strict";m.r(t);var _=function(){var e=this,t=e._self._c;return t("div",{staticClass:"container"},[e._m(0),t("div",{staticClass:"content"},[t("div",{staticClass:"metric"},[e._v(" 视角 "),t("a-select",{staticStyle:{width:"180px","font-size":"14px","margin-left":"10px"},attrs:{"default-value":"MAE12"},on:{change:e.metricsChange}},[t("a-select-option",{attrs:{value:"MAE12"}},[e._v(" MAE @ 12 STEP ")]),t("a-select-option",{attrs:{value:"MAPE12"}},[e._v(" MAPE @ 12 STEP ")]),t("a-select-option",{attrs:{value:"RMSE12"}},[e._v(" RMSE @ 12 STEP ")])],1)],1),t("div",{staticClass:"model-ranking"},[t("table",[t("thead",{staticStyle:{"font-size":"16px"}},[t("tr",[t("th",[e._v("排名")]),t("th",[e._v("模型")]),t("th",[e._v("论文")]),t("th",[e._v("年份")]),t("th",[e._v("3 STEP"),t("a-button",{staticStyle:{color:"white"},attrs:{type:"link",icon:"caret-down",size:"small"},on:{click:e.sortBy3}})],1),t("th",[e._v("6 STEP"),t("a-button",{staticStyle:{color:"white"},attrs:{type:"link",icon:"caret-down",size:"small"},on:{click:e.sortBy6}})],1),t("th",[e._v("9 STEP"),t("a-button",{staticStyle:{color:"white"},attrs:{type:"link",icon:"caret-down",size:"small"},on:{click:e.sortBy9}})],1),t("th",[e._v("12 STEP"),t("a-button",{staticStyle:{color:"white"},attrs:{type:"link",icon:"caret-down",size:"small"},on:{click:e.sortBy12}})],1)])]),t("tbody",e._l(e.rankingData,(function(m){return t("tr",{key:m.model},[t("td",{attrs:{width:"6%"}},[e._v(e._s(m.rank))]),t("td",{attrs:{width:"8%"}},[t("a",{attrs:{href:m.mlink,target:"_blank"}},[e._v(e._s(m.model))])]),t("td",{attrs:{width:"33%"}},[t("a",{staticClass:"paper",attrs:{href:m.plink,target:"_blank"}},[e._v(e._s(m.paper))])]),t("td",{attrs:{width:"5%"}},[e._v(e._s(m.year))]),t("td",{attrs:{width:"12%"}},[e._v(e._s(m.step3))]),t("td",{attrs:{width:"12%"}},[e._v(e._s(m.step6))]),t("td",{attrs:{width:"12%"}},[e._v(e._s(m.step9))]),t("td",{attrs:{width:"12%"}},[e._v(e._s(m.step12))])])})),0)])]),t("br"),t("br")])])},s=[function(){var e=this,t=e._self._c;return t("div",{staticClass:"header"},[t("div",{staticStyle:{"padding-top":"20px",color:"white"}},[t("p",{staticStyle:{margin:"20px 0 30px 130px","font-size":"60px"}},[e._v("PEMS-BAY")])])])}];m("a71c"),m("d24d");const p=[{rank:0,model:"GWNET",mlink:"#",paper:"Graph Wavenet for Deep Spatial-Temporal Graph Modeling",plink:"https://arxiv.org/abs/1906.00121",year:"2019",step3:1.317233086,step6:1.634672761,step9:1.802474618,step12:1.914417505,m_mae_step1:.857503414,m_mae_step2:1.129513025,m_mae_step3:1.317233086,m_mae_step4:1.45383203,m_mae_step5:1.555249095,m_mae_step6:1.634672761,m_mae_step7:1.700960994,m_mae_step8:1.755648851,m_mae_step9:1.802474618,m_mae_step10:1.843832612,m_mae_step11:1.879278898,m_mae_step12:1.914417505,m_mape_step1:.016505523,m_mape_step2:.022771893,m_mape_step3:.027564418,m_mape_step4:.031350676,m_mape_step5:.034270905,m_mape_step6:.03663848,m_mape_step7:.03858446,m_mape_step8:.040278487,m_mape_step9:.041706078,m_mape_step10:.042936664,m_mape_step11:.044025842,m_mape_step12:.045043543,m_rmse_step1:1.557686806,m_rmse_step2:2.254713058,m_rmse_step3:2.78249383,m_rmse_step4:3.177927256,m_rmse_step5:3.47552228,m_rmse_step6:3.703932285,m_rmse_step7:3.889703751,m_rmse_step8:4.036758423,m_rmse_step9:4.154026985,m_rmse_step10:4.251091957,m_rmse_step11:4.327308178,m_rmse_step12:4.404269218},{rank:0,model:"MTGNN",mlink:"#",paper:"Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks",plink:"https://arxiv.org/abs/2005.11650",year:"2020",step3:1.330717564,step6:1.65684998,step9:1.830839753,step12:1.954051137,m_mae_step1:.868918359,m_mae_step2:1.140851259,m_mae_step3:1.330717564,m_mae_step4:1.469416142,m_mae_step5:1.57374084,m_mae_step6:1.65684998,m_mae_step7:1.725528359,m_mae_step8:1.782338262,m_mae_step9:1.830839753,m_mae_step10:1.87438798,m_mae_step11:1.913544536,m_mae_step12:1.954051137,m_mape_step1:.01668698,m_mape_step2:.022997182,m_mape_step3:.027925584,m_mape_step4:.031782493,m_mape_step5:.034853522,m_mape_step6:.037321355,m_mape_step7:.03941799,m_mape_step8:.041145034,m_mape_step9:.042609747,m_mape_step10:.043912422,m_mape_step11:.045049824,m_mape_step12:.046192467,m_rmse_step1:1.566528082,m_rmse_step2:2.260257483,m_rmse_step3:2.797163725,m_rmse_step4:3.210089922,m_rmse_step5:3.520379782,m_rmse_step6:3.759927511,m_rmse_step7:3.948573112,m_rmse_step8:4.095731258,m_rmse_step9:4.214382172,m_rmse_step10:4.316540241,m_rmse_step11:4.403265476,m_rmse_step12:4.488767147},{rank:0,model:"DCRNN",mlink:"#",paper:"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting",plink:"https://arxiv.org/abs/1707.01926",year:"2018",step3:1.313929796,step6:1.652003407,step9:1.841412902,step12:1.96604085,m_mae_step1:.848314524,m_mae_step2:1.124167085,m_mae_step3:1.313929796,m_mae_step4:1.455781221,m_mae_step5:1.564353466,m_mae_step6:1.652003407,m_mae_step7:1.725697517,m_mae_step8:1.787886858,m_mae_step9:1.841412902,m_mae_step10:1.887333989,m_mae_step11:1.927678704,m_mae_step12:1.96604085,m_mape_step1:.016326521,m_mape_step2:.022630956,m_mape_step3:.027422428,m_mape_step4:.031334415,m_mape_step5:.034503739,m_mape_step6:.037152618,m_mape_step7:.039443508,m_mape_step8:.04138767,m_mape_step9:.043020427,m_mape_step10:.044378143,m_mape_step11:.045528214,m_mape_step12:.046587929,m_rmse_step1:1.537957907,m_rmse_step2:2.234685183,m_rmse_step3:2.774805546,m_rmse_step4:3.195387363,m_rmse_step5:3.52022028,m_rmse_step6:3.776569843,m_rmse_step7:3.986632824,m_rmse_step8:4.159185886,m_rmse_step9:4.300885677,m_rmse_step10:4.416303635,m_rmse_step11:4.513619423,m_rmse_step12:4.600404739},{rank:0,model:"AGCRN",mlink:"#",paper:"Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting",plink:"https://arxiv.org/abs/2007.02842",year:"2020",step3:1.367542028,step6:1.68644464,step9:1.844792604,step12:1.966456294,m_mae_step1:.943254411,m_mae_step2:1.186352849,m_mae_step3:1.367542028,m_mae_step4:1.504779339,m_mae_step5:1.606099129,m_mae_step6:1.68644464,m_mae_step7:1.74995029,m_mae_step8:1.802184224,m_mae_step9:1.844792604,m_mae_step10:1.882825017,m_mae_step11:1.921007872,m_mae_step12:1.966456294,m_mape_step1:.0191298,m_mape_step2:.024724899,m_mape_step3:.029343201,m_mape_step4:.03307645,m_mape_step5:.035824399,m_mape_step6:.0380367,m_mape_step7:.039836947,m_mape_step8:.041422024,m_mape_step9:.042698178,m_mape_step10:.043733742,m_mape_step11:.0447708,m_mape_step12:.045890179,m_rmse_step1:1.721888065,m_rmse_step2:2.354927063,m_rmse_step3:2.867988348,m_rmse_step4:3.27675724,m_rmse_step5:3.587590933,m_rmse_step6:3.827363014,m_rmse_step7:4.006102085,m_rmse_step8:4.144904613,m_rmse_step9:4.265449524,m_rmse_step10:4.374513626,m_rmse_step11:4.475839615,m_rmse_step12:4.587117672},{rank:0,model:"STGCN",mlink:"#",paper:"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting",plink:"https://www.ijcai.org/Proceedings/2018/0505",year:"2018",step3:1.450409293,step6:1.767900348,step9:1.940572739,step12:2.056560993,m_mae_step1:1.241303444,m_mae_step2:1.272721767,m_mae_step3:1.450409293,m_mae_step4:1.583973408,m_mae_step5:1.687048912,m_mae_step6:1.767900348,m_mae_step7:1.835595489,m_mae_step8:1.893149614,m_mae_step9:1.940572739,m_mae_step10:1.984394789,m_mae_step11:2.02146101,m_mae_step12:2.056560993,m_mape_step1:.026208749,m_mape_step2:.026164116,m_mape_step3:.030696817,m_mape_step4:.034198273,m_mape_step5:.037147429,m_mape_step6:.039210107,m_mape_step7:.041710436,m_mape_step8:.042829249,m_mape_step9:.04450424,m_mape_step10:.04560598,m_mape_step11:.04678899,m_mape_step12:.047559738,m_rmse_step1:2.13414669,m_rmse_step2:2.383548021,m_rmse_step3:2.871806383,m_rmse_step4:3.241825104,m_rmse_step5:3.525333643,m_rmse_step6:3.742093563,m_rmse_step7:3.906446457,m_rmse_step8:4.043199062,m_rmse_step9:4.139858246,m_rmse_step10:4.22990799,m_rmse_step11:4.291146755,m_rmse_step12:4.355161667},{rank:0,model:"GMAN",mlink:"#",paper:"GMAN: A Graph Multi-Attention Network for Traffic Prediction",plink:"https://ojs.aaai.org//index.php/AAAI/article/view/5477",year:"2020",step3:1.521445155,step6:1.828175068,step9:1.998253703,step12:2.114692688,m_mae_step1:1.125500441,m_mae_step2:1.353003979,m_mae_step3:1.521445155,m_mae_step4:1.648182511,m_mae_step5:1.748183846,m_mae_step6:1.828175068,m_mae_step7:1.893953443,m_mae_step8:1.949676514,m_mae_step9:1.998253703,m_mae_step10:2.040922403,m_mae_step11:2.078896046,m_mae_step12:2.114692688,m_mape_step1:.022199463,m_mape_step2:.027716596,m_mape_step3:.031994887,m_mape_step4:.035366327,m_mape_step5:.038103811,m_mape_step6:.040306974,m_mape_step7:.042137384,m_mape_step8:.043649245,m_mape_step9:.044952124,m_mape_step10:.046072811,m_mape_step11:.047064614,m_mape_step12:.048016075,m_rmse_step1:1.939200044,m_rmse_step2:2.502240419,m_rmse_step3:2.950233459,m_rmse_step4:3.282125711,m_rmse_step5:3.535974264,m_rmse_step6:3.733086109,m_rmse_step7:3.882786036,m_rmse_step8:4.005829811,m_rmse_step9:4.107138157,m_rmse_step10:4.190578461,m_rmse_step11:4.260962486,m_rmse_step12:4.321457863},{rank:0,model:"ASTGCN",mlink:"#",paper:"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting",plink:"https://ojs.aaai.org//index.php/AAAI/article/view/3881",year:"2019",step3:1.496554732,step6:1.954262972,step9:2.253344536,step12:2.522253752,m_mae_step1:.959167957,m_mae_step2:1.265708447,m_mae_step3:1.496554732,m_mae_step4:1.682306528,m_mae_step5:1.826324224,m_mae_step6:1.954262972,m_mae_step7:2.063279152,m_mae_step8:2.160314798,m_mae_step9:2.253344536,m_mae_step10:2.347047329,m_mae_step11:2.433424234,m_mae_step12:2.522253752,m_mape_step1:.019549424,m_mape_step2:.026637457,m_mape_step3:.032356605,m_mape_step4:.037207827,m_mape_step5:.041132949,m_mape_step6:.044519011,m_mape_step7:.047384713,m_mape_step8:.049868591,m_mape_step9:.052266583,m_mape_step10:.05448395,m_mape_step11:.056706849,m_mape_step12:.058882017,m_rmse_step1:1.75325191,m_rmse_step2:2.465423584,m_rmse_step3:3.023877382,m_rmse_step4:3.462608576,m_rmse_step5:3.80658102,m_rmse_step6:4.090635777,m_rmse_step7:4.327079773,m_rmse_step8:4.528690338,m_rmse_step9:4.708185196,m_rmse_step10:4.871789932,m_rmse_step11:5.027686596,m_rmse_step12:5.171713352},{rank:0,model:"GRU",mlink:"#",paper:"Using LSTM and GRU neural network methods for traffic flow prediction",plink:"https://ieeexplore.ieee.org/abstract/document/7804912",year:"2016",step3:2.490625381,step6:2.508490086,step9:2.535393953,step12:2.57500267,m_mae_step1:2.480051517,m_mae_step2:2.482344627,m_mae_step3:2.490625381,m_mae_step4:2.496524811,m_mae_step5:2.500797272,m_mae_step6:2.508490086,m_mae_step7:2.516708612,m_mae_step8:2.525750875,m_mae_step9:2.535393953,m_mae_step10:2.546352625,m_mae_step11:2.558960199,m_mae_step12:2.57500267,m_mape_step1:.058738798,m_mape_step2:.058935836,m_mape_step3:.059239618,m_mape_step4:.059515335,m_mape_step5:.059768926,m_mape_step6:.060103532,m_mape_step7:.06045866,m_mape_step8:.060867239,m_mape_step9:.061306074,m_mape_step10:.061785437,m_mape_step11:.062330086,m_mape_step12:.06308227,m_rmse_step1:5.133162975,m_rmse_step2:5.169201374,m_rmse_step3:5.204359531,m_rmse_step4:5.2340765,m_rmse_step5:5.25694561,m_rmse_step6:5.287732124,m_rmse_step7:5.318385124,m_rmse_step8:5.350336075,m_rmse_step9:5.383542538,m_rmse_step10:5.419529438,m_rmse_step11:5.458177567,m_rmse_step12:5.509566784},{rank:0,model:"Seq2Seq",mlink:"#",paper:"Sequence to Sequence Learning with Neural Networks",plink:"https://papers.nips.cc/paper/2014/hash/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html",year:"2014",step3:2.442513943,step6:2.446320295,step9:2.492647886,step12:2.580511332,m_mae_step1:2.545048952,m_mae_step2:2.457653999,m_mae_step3:2.442513943,m_mae_step4:2.440240383,m_mae_step5:2.441186905,m_mae_step6:2.446320295,m_mae_step7:2.457454681,m_mae_step8:2.472849846,m_mae_step9:2.492647886,m_mae_step10:2.516484976,m_mae_step11:2.545600414,m_mae_step12:2.580511332,m_mape_step1:.061583076,m_mape_step2:.058504902,m_mape_step3:.057939678,m_mape_step4:.057794452,m_mape_step5:.05780055,m_mape_step6:.057948768,m_mape_step7:.058178008,m_mape_step8:.058471423,m_mape_step9:.05886057,m_mape_step10:.059357226,m_mape_step11:.059984792,m_mape_step12:.060759738,m_rmse_step1:5.211314678,m_rmse_step2:5.129349709,m_rmse_step3:5.107882023,m_rmse_step4:5.116268635,m_rmse_step5:5.124783039,m_rmse_step6:5.144079208,m_rmse_step7:5.173463821,m_rmse_step8:5.211371422,m_rmse_step9:5.25851965,m_rmse_step10:5.315991879,m_rmse_step11:5.385982513,m_rmse_step12:5.470291138},{rank:0,model:"AE",mlink:"#",paper:"Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction",plink:"https://ieeexplore.ieee.org/document/6910027",year:"2014",step3:2.569958448,step6:2.572859526,step9:2.626580238,step12:2.723689795,m_mae_step1:2.59362483,m_mae_step2:2.579221249,m_mae_step3:2.569958448,m_mae_step4:2.565395594,m_mae_step5:2.565946579,m_mae_step6:2.572859526,m_mae_step7:2.585615635,m_mae_step8:2.603767157,m_mae_step9:2.626580238,m_mae_step10:2.654663563,m_mae_step11:2.686751127,m_mae_step12:2.723689795,m_mape_step1:.063843451,m_mape_step2:.063234784,m_mape_step3:.062807344,m_mape_step4:.062561639,m_mape_step5:.062489022,m_mape_step6:.062600054,m_mape_step7:.062902927,m_mape_step8:.063357234,m_mape_step9:.063946806,m_mape_step10:.064680532,m_mape_step11:.065536693,m_mape_step12:.066535003,m_rmse_step1:5.370556831,m_rmse_step2:5.330964565,m_rmse_step3:5.30209589,m_rmse_step4:5.285309792,m_rmse_step5:5.279687405,m_rmse_step6:5.288401127,m_rmse_step7:5.310306549,m_rmse_step8:5.345190525,m_rmse_step9:5.391934872,m_rmse_step10:5.45192337,m_rmse_step11:5.523836136,m_rmse_step12:5.608017921},{rank:0,model:"STG2Seq",mlink:"#",paper:"STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting",plink:"https://arxiv.org/abs/1905.10069",year:"2019",step3:2.192012072,step6:2.424111366,step9:2.604333401,step12:2.768119335,m_mae_step1:1.908781528,m_mae_step2:2.041549683,m_mae_step3:2.192012072,m_mae_step4:2.273479939,m_mae_step5:2.363734484,m_mae_step6:2.424111366,m_mae_step7:2.492698908,m_mae_step8:2.545662642,m_mae_step9:2.604333401,m_mae_step10:2.656032801,m_mae_step11:2.712737799,m_mae_step12:2.768119335,m_mape_step1:.042424567,m_mape_step2:.046142429,m_mape_step3:.050303444,m_mape_step4:.052709654,m_mape_step5:.0552302,m_mape_step6:.056911804,m_mape_step7:.058803353,m_mape_step8:.060218289,m_mape_step9:.061782066,m_mape_step10:.063095964,m_mape_step11:.0645357,m_mape_step12:.065888219,m_rmse_step1:3.572165012,m_rmse_step2:3.870104074,m_rmse_step3:4.230829716,m_rmse_step4:4.448564529,m_rmse_step5:4.674290657,m_rmse_step6:4.826023102,m_rmse_step7:4.995254993,m_rmse_step8:5.124192715,m_rmse_step9:5.265581131,m_rmse_step10:5.386611462,m_rmse_step11:5.520673275,m_rmse_step12:5.650220871},{rank:0,model:"TGCN",mlink:"#",paper:"T-gcn: A temporal graph convolutional network for traffic prediction",plink:"https://ieeexplore.ieee.org/abstract/document/8809901/",year:"2020",step3:2.632635593,step6:2.739435196,step9:2.906158447,step12:3.103404999,m_mae_step1:2.587588787,m_mae_step2:2.60854578,m_mae_step3:2.632635593,m_mae_step4:2.661967993,m_mae_step5:2.696000099,m_mae_step6:2.739435196,m_mae_step7:2.788401604,m_mae_step8:2.843111515,m_mae_step9:2.906158447,m_mae_step10:2.969862461,m_mae_step11:3.03798604,m_mae_step12:3.103404999,m_mape_step1:.062867194,m_mape_step2:.063152783,m_mape_step3:.063606896,m_mape_step4:.064293422,m_mape_step5:.065137349,m_mape_step6:.06620612,m_mape_step7:.067390382,m_mape_step8:.068846896,m_mape_step9:.070369221,m_mape_step10:.072067507,m_mape_step11:.073858663,m_mape_step12:.07576818,m_rmse_step1:5.228998661,m_rmse_step2:5.247745514,m_rmse_step3:5.287768841,m_rmse_step4:5.350580692,m_rmse_step5:5.429993153,m_rmse_step6:5.525117874,m_rmse_step7:5.6279459,m_rmse_step8:5.751735687,m_rmse_step9:5.874735832,m_rmse_step10:6.014170647,m_rmse_step11:6.158379555,m_rmse_step12:6.314451694}];var a={data(){return{PEMS_BAY_origin:p,rankingData:[],metrics:"MAE12"}},components:{},mounted(){this.rankingData=p,this.metricsChange("MAE12")},methods:{sortBy3(){this.rankingData.sort((function(e,t){return e.step3-t.step3}));let e=0;this.rankingData.forEach(t=>{e+=1,t.rank=e})},sortBy6(){this.rankingData.sort((function(e,t){return e.step6-t.step6}));let e=0;this.rankingData.forEach(t=>{e+=1,t.rank=e})},sortBy9(){this.rankingData.sort((function(e,t){return e.step9-t.step9}));let e=0;this.rankingData.forEach(t=>{e+=1,t.rank=e})},sortBy12(){this.rankingData.sort((function(e,t){return e.step12-t.step12}));let e=0;this.rankingData.forEach(t=>{e+=1,t.rank=e})},metricsChange(e){"MAE12"==e?this.rankingData.forEach(e=>{e.step3=e.m_mae_step3.toFixed(5),e.step6=e.m_mae_step6.toFixed(5),e.step9=e.m_mae_step9.toFixed(5),e.step12=e.m_mae_step12.toFixed(5)}):"MAPE12"==e?this.rankingData.forEach(e=>{e.step3=e.m_mape_step3.toFixed(5),e.step6=e.m_mape_step6.toFixed(5),e.step9=e.m_mape_step9.toFixed(5),e.step12=e.m_mape_step12.toFixed(5)}):"RMSE12"==e&&this.rankingData.forEach(e=>{e.step3=e.m_rmse_step3.toFixed(5),e.step6=e.m_rmse_step6.toFixed(5),e.step9=e.m_rmse_step9.toFixed(5),e.step12=e.m_rmse_step12.toFixed(5)}),this.sortBy12()}}},r=a,n=(m("3f7a"),m("1805")),o=Object(n["a"])(r,_,s,!1,null,"1013a361",null);t["default"]=o.exports},9134:function(e,t,m){"use strict";var _=m("72a9"),s=m("90ff"),p=m("0b9b");e.exports=function(e,t,m){_?s.f(e,t,p(0,m)):e[t]=m}},"91aba":function(e,t,m){"use strict";var _=m("d5e2"),s=m("2b31"),p=m("9918");e.exports=function(e,t,m){var a,r;s(e);try{if(a=p(e,"return"),!a){if("throw"===t)throw m;return m}a=_(a,e)}catch(n){r=!0,a=n}if("throw"===t)throw m;if(r)throw a;return s(a),m}},"9d39":function(e,t,m){},a71c:function(e,t,m){"use strict";m("484e")},b3ba:function(e,t,m){"use strict";var _=m("2e7c");e.exports=_("document","documentElement")},cc1f:function(e,t,m){"use strict";e.exports=function(e){return{iterator:e,next:e.next,done:!1}}},d24d:function(e,t,m){"use strict";m("e42d")},d468:function(e,t,m){"use strict";var _=m("dbfb"),s=m("d5e2"),p=m("2b31"),a=m("d5d3"),r=m("e63b"),n=m("6a92"),o=m("eae2"),i=m("f9d8"),c=m("83b8"),u=m("91aba"),f=TypeError,l=function(e,t){this.stopped=e,this.result=t},d=l.prototype;e.exports=function(e,t,m){var h,v,k,b,w,y,g,x=m&&m.that,E=!(!m||!m.AS_ENTRIES),S=!(!m||!m.IS_RECORD),A=!(!m||!m.IS_ITERATOR),T=!(!m||!m.INTERRUPTED),D=_(t,x),M=function(e){return h&&u(h,"normal",e),new l(!0,e)},P=function(e){return E?(p(e),T?D(e[0],e[1],M):D(e[0],e[1])):T?D(e,M):D(e)};if(S)h=e.iterator;else if(A)h=e;else{if(v=c(e),!v)throw new f(a(e)+" is not iterable");if(r(v)){for(k=0,b=n(e);b>k;k++)if(w=P(e[k]),w&&o(d,w))return w;return new l(!1)}h=i(e,v)}y=S?e.next:h.next;while(!(g=s(y,h)).done){try{w=P(g.value)}catch(R){u(h,"throw",R)}if("object"==typeof w&&w&&o(d,w))return w}return new l(!1)}},dbfb:function(e,t,m){"use strict";var _=m("71f6"),s=m("fb2c"),p=m("c182"),a=_(_.bind);e.exports=function(e,t){return s(e),void 0===t?e:p?a(e,t):function(){return e.apply(t,arguments)}}},dc72:function(e,t,m){"use strict";e.exports={}},e42d:function(e,t,m){"use strict";var _=m("8922"),s=m("d468"),p=m("fb2c"),a=m("2b31"),r=m("cc1f");_({target:"Iterator",proto:!0,real:!0},{forEach:function(e){a(this),p(e);var t=r(this),m=0;s(t,(function(t){e(t,m++)}),{IS_RECORD:!0})}})},e63b:function(e,t,m){"use strict";var _=m("1794"),s=m("dc72"),p=_("iterator"),a=Array.prototype;e.exports=function(e){return void 0!==e&&(s.Array===e||a[p]===e)}},f0dc:function(e,t,m){"use strict";var _=m("140c"),s=m("6403"),p=m("f7d2"),a=m("379e"),r=m("5d32"),n=a("IE_PROTO"),o=Object,i=o.prototype;e.exports=r?o.getPrototypeOf:function(e){var t=p(e);if(_(t,n))return t[n];var m=t.constructor;return s(m)&&t instanceof m?m.prototype:t instanceof o?i:null}},f9d8:function(e,t,m){"use strict";var _=m("d5e2"),s=m("fb2c"),p=m("2b31"),a=m("d5d3"),r=m("83b8"),n=TypeError;e.exports=function(e,t){var m=arguments.length<2?r(e):t;if(s(m))return p(_(m,e));throw new n(a(e)+" is not iterable")}},ffe2:function(e,t,m){"use strict";var _,s,p,a=m("d3d6"),r=m("6403"),n=m("23d7"),o=m("8bd3"),i=m("f0dc"),c=m("344d"),u=m("1794"),f=m("6bcb"),l=u("iterator"),d=!1;[].keys&&(p=[].keys(),"next"in p?(s=i(i(p)),s!==Object.prototype&&(_=s)):d=!0);var h=!n(_)||a((function(){var e={};return _[l].call(e)!==e}));h?_={}:f&&(_=o(_)),r(_[l])||c(_,l,(function(){return this})),e.exports={IteratorPrototype:_,BUGGY_SAFARI_ITERATORS:d}}}]);
//# sourceMappingURL=chunk-5989ee3a.c083a877.js.map