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10.1016/j.annals.2020.103117 http://scihub22266oqcxt.onion/10.1016/j.annals.2020.103117 33518847!7832145!33518847     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       Ann+Tour+Res 2021 ; 87 (ä): 103117 Nephropedia Template TP {{tp|p=33518847|t=2021. The good, the bad and the ugly on COVID-19 tourism recovery |pdf=|usr=}}{{33518847}} gab.com Text The good, the bad and the ugly on COVID-19 tourism recovery JO: Ann Tour Res http://www.kidney.de/pget.php?&tw=1&pmid=33518847 #FOAvip This paper is to produce different scenarios in forecasts for international tourism demand, in light of the COVID-19 pandemic. By implementing two distinct methodologies (the Long Short Term Memory neural network and the Generalized Additive Model), based on recent crises, we are able to calculate the expected drop in the international tourist arrivals for the next 12 months. We use a rolling-window testing strategy to calculate accuracy metrics and show that even though all models have comparable accuracy, the forecasts produced vary significantly according to the training data set, a finding that should be alarming to researchers. Our results indicate that the drop in tourist arrivals can range between 30.8% and 76.3% and will persist at least until June 2021. Twit Text FOAVip The good, the bad and the ugly on COVID-19 tourism recovery ????Ann Tour Res http://www.kidney.de/pget.php?&tw=1&pmid=33518847 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33518847 #FOAvip English Wikipedia <ref>{{Cite pmid|33518847}}</ref> The good, the bad and the ugly on COVID-19 tourism recovery #MMPMID33518847 Fotiadis A; Polyzos S; Huan TTC Ann Tour Res 2021[Mar]; 87 (ä): 103117 PMID33518847show ga This paper is to produce different scenarios in forecasts for international tourism demand, in light of the COVID-19 pandemic. By implementing two distinct methodologies (the Long Short Term Memory neural network and the Generalized Additive Model), based on recent crises, we are able to calculate the expected drop in the international tourist arrivals for the next 12 months. We use a rolling-window testing strategy to calculate accuracy metrics and show that even though all models have comparable accuracy, the forecasts produced vary significantly according to the training data set, a finding that should be alarming to researchers. Our results indicate that the drop in tourist arrivals can range between 30.8% and 76.3% and will persist at least until June 2021. äThe good, the bad and the ugly on COVID-19 tourism recovery (epmc).pdf DeepDyve Pubget Overpricing