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+Problem.+Recent+research+has+abandoned+Gated+Recurrent+Units+(GRU)+and+utilized+dilated+convolutions+or+temporal+slicing+for+feature+extraction%252C+and+they+have+the+following+drawbacks%3a+(1)+Dilated+convolutions+fail+to+capture+the+features+of+adjacent+time+steps%252C+resulting+in+the+loss+of+crucial+transitional+data.+(2)+The+connections+within+the+same+temporal+slice+are+strong%252C+while+the+connections+between+different+temporal+slices+are+too+loose.+In+light+of+these+limitations%252C+we+emphasize+the+importance+of+analyzing+a+complete+time+series+repeatedly+and+the+crucial+role+of+GRU+in+MTS.+Therefore%252C+we+propose+SGRU%3a+Structured+Gated+Recurrent+Units%252C+which+involve+structured+GRU+layers+and+non-linear+units%252C+along+with+multiple+layers+of+time+embedding+to+enhance+the+model%27s+fitting+performance.+We+evaluate+our+approach+on+four+publicly+available+California+traffic+datasets%3a+PeMS03%252C+PeMS04%252C+PeMS07%252C+and+PeMS08+for+regression+prediction.+Experimental+results+demonstrate+that+our+model+outperforms+baseline+models+with+average+improvements+of+11.7%2525%252C+18.6%2525%252C+18.5%2525%252C+and+12.0%2525+respectively.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.e2f96d2d58bbfb2f9f04bc4e9c2b31ef%3frik%3dGACMlNdi07KVcQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Sgru+Refinery+Diagram&FORM=IRPRST&ck=8857898ABF2D7C91E6939C18CAEAD413&selectedIndex=9&itb=0)
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