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10.1007/s40747-021-00289-x http://scihub22266oqcxt.onion/10.1007/s40747-021-00289-x C7921832/?report=reader!7921832!34777958     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Complex+Intell+Systems 2023 ; 9 (3): 2285-95 Nephropedia Template TP {{tp|p=34777958|t=2023. Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology |pdf=|usr=}}{{34777958}} gab.com Text Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology JO: Complex Intell Systems http://www.kidney.de/pget.php?&tw=1&pmid=34777958 #FOAvip Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models. Twit Text FOAVip Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology ????Complex Intell Systems http://www.kidney.de/pget.php?&tw=1&pmid=34777958 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34777958 #FOAvip English Wikipedia <ref>{{Cite pmid|34777958}}</ref> Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology #MMPMID34777958 Jiang-ning L; Xian-liang S; An-qiang H; Ze-fang H; Yu-xuan K; Dong L Complex Intell Systems 2023[]; 9 (3): 2285-95 PMID34777958show ga Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models. äForecasting emergency medicine reserve demand with a novel decompositi(epmc).pdf DeepDyve Pubget Overpricing