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10.1016/j.chaos.2020.110215 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.110215 32839644!7437517!32839644     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chaos+Solitons+Fractals 2020 ; 140 (ä): 110215 Nephropedia Template TP {{tp|p=32839644|t=2020. Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities |pdf=|usr=}}{{32839644}} gab.com Text Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32839644 #FOAvip Over the past few years, the application of deep learning models to finance has received much attention from investors and researchers. Our work continues this trend, presenting an application of a Deep learning model, long-term short-term memory (LSTM), for the forecasting of commodity prices. The obtained results predict with great accuracy the prices of commodities including crude oil price (98.2 price(88.2 on the variability of the commodity prices. This involved checking at the correlation and the causality with the Ganger Causality method. Our results reveal that the coronavirus impacts the recent variability of commodity prices through the number of confirmed cases and the total number of deaths. We then investigate a hybrid ARIMA-Wavelet model to forecast the coronavirus spread. This analyses is interesting as a consequence of the strong causal relationship between the coronavirus(number of confirmed cases) and the commodity prices, the prediction of the evolution of COVID-19 can be useful to anticipate the future direction of the commodity prices. Twit Text FOAVip Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32839644 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32839644 #FOAvip English Wikipedia <ref>{{Cite pmid|32839644}}</ref> Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities #MMPMID32839644 Sadefo Kamdem J; Bandolo Essomba R; Njong Berinyuy J Chaos Solitons Fractals 2020[Nov]; 140 (ä): 110215 PMID32839644show ga Over the past few years, the application of deep learning models to finance has received much attention from investors and researchers. Our work continues this trend, presenting an application of a Deep learning model, long-term short-term memory (LSTM), for the forecasting of commodity prices. The obtained results predict with great accuracy the prices of commodities including crude oil price (98.2 price(88.2 on the variability of the commodity prices. This involved checking at the correlation and the causality with the Ganger Causality method. Our results reveal that the coronavirus impacts the recent variability of commodity prices through the number of confirmed cases and the total number of deaths. We then investigate a hybrid ARIMA-Wavelet model to forecast the coronavirus spread. This analyses is interesting as a consequence of the strong causal relationship between the coronavirus(number of confirmed cases) and the commodity prices, the prediction of the evolution of COVID-19 can be useful to anticipate the future direction of the commodity prices. äDeep learning models for forecasting and analyzing the implications of(epmc).pdf DeepDyve Pubget Overpricing