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10.1016/j.ijforecast.2020.09.003 http://scihub22266oqcxt.onion/10.1016/j.ijforecast.2020.09.003 32952247!7486833!32952247     free     free     free       Int+J+Forecast 2022 ; 38 (2): 453-466 Nephropedia Template TP {{tp|p=32952247|t=2022. Short-term forecasting of the coronavirus pandemic |pdf=|usr=}}{{32952247}} gab.com Text Short-term forecasting of the coronavirus pandemic JO: Int J Forecast http://www.kidney.de/pget.php?&tw=1&pmid=32952247 #FOAvip We have been publishing real-time forecasts of confirmed cases and deaths from coronavirus disease 2019 (COVID-19) since mid-March 2020 (published at www.doornik.com/COVID-19). These forecasts are short-term statistical extrapolations of past and current data. They assume that the underlying trend is informative regarding short-term developments but without requiring other assumptions about how the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is spreading, or whether preventative policies are effective. Thus, they are complementary to the forecasts obtained from epidemiological models. The forecasts are based on extracting trends from windows of data using machine learning and then computing the forecasts by applying some constraints to the flexible extracted trend. These methods have been applied previously to various other time series data and they performed well. They have also proved effective in the COVID-19 setting where they provided better forecasts than some epidemiological models in the earlier stages of the pandemic. Twit Text FOAVip Short-term forecasting of the coronavirus pandemic ????Int J Forecast http://www.kidney.de/pget.php?&tw=1&pmid=32952247 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32952247 #FOAvip English Wikipedia <ref>{{Cite pmid|32952247}}</ref> Short-term forecasting of the coronavirus pandemic #MMPMID32952247 Doornik JA; Castle JL; Hendry DF Int J Forecast 2022[Apr]; 38 (2): 453-466 PMID32952247show ga We have been publishing real-time forecasts of confirmed cases and deaths from coronavirus disease 2019 (COVID-19) since mid-March 2020 (published at www.doornik.com/COVID-19). These forecasts are short-term statistical extrapolations of past and current data. They assume that the underlying trend is informative regarding short-term developments but without requiring other assumptions about how the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is spreading, or whether preventative policies are effective. Thus, they are complementary to the forecasts obtained from epidemiological models. The forecasts are based on extracting trends from windows of data using machine learning and then computing the forecasts by applying some constraints to the flexible extracted trend. These methods have been applied previously to various other time series data and they performed well. They have also proved effective in the COVID-19 setting where they provided better forecasts than some epidemiological models in the earlier stages of the pandemic. äShort-term forecasting of the coronavirus pandemic (epmc).pdf DeepDyve Pubget Overpricing