Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources.Due to the exponential spread of the COVID-19...Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources.Due to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature.To accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access dataset.Our objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms.Specifically,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset.Considering the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,respectively.On the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,respectively.For 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,respectively.We found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 transmission.We also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.展开更多
In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting,this brief study analyzes the predictability of Bitcoin volume and returns using Google search valu...In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting,this brief study analyzes the predictability of Bitcoin volume and returns using Google search values.We employed a rich set of established empirical approaches,including a VAR framework,a copulas approach,and non-parametric drawings,to capture a dependence structure.Using a weekly dataset from 2013 to 2017,our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume.Shocks to search values have a positive effect,which persisted for at least a week.Our findings contribute to the debate on cryptocurrencies/Bitcoins and have profound implications in terms of understanding their dynamics,which are of special interest to investors and economic policymakers.展开更多
The valuation of cryptocurrencies is important given the increasing significance of this potential asset class.However,most state-of-the-art cryptocurrency valuation methods only focus on one of the fundamental factor...The valuation of cryptocurrencies is important given the increasing significance of this potential asset class.However,most state-of-the-art cryptocurrency valuation methods only focus on one of the fundamental factors or sentiments and use out-of-date data sources.In this study,a robust cryptocurrency valuation method is developed using up-to-date datasets.Using various panel regression models and moving-window regression tests,the impacts of fundamental factors and sentiments in the valuation of cryptocurrencies are explored with data covering from January 1,2009 to April 30,2023.The research shows the importance of sentiments and suggests that the fear and greed index can indicate when to make a cryptocurrency investment,while Google search interest in cryptocurrency is crucial when choosing the appropriate type of cryptocurrency.Moreover,consensus mechanism and initial coin offering have significant effects on cryptocurrencies without stablecoins,while their impacts on cryptocurrencies with stablecoins are insignificant.Other fundamental factors,such as the type of supply and the presence of smart contracts,do not have a significant influence on cryptocurrency.Findings from this study can enhance cryptocurrency marketisation and provide insightful guidance for investors,portfolio managers,and policymakers in assessing the utility level of each cryptocurrency.展开更多
基金This work is supported in part by the Deanship of Scientific Research at Jouf University under Grant No.(CV-28–41).
文摘Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources.Due to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature.To accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access dataset.Our objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms.Specifically,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset.Considering the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,respectively.On the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,respectively.For 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,respectively.We found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 transmission.We also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.
文摘In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting,this brief study analyzes the predictability of Bitcoin volume and returns using Google search values.We employed a rich set of established empirical approaches,including a VAR framework,a copulas approach,and non-parametric drawings,to capture a dependence structure.Using a weekly dataset from 2013 to 2017,our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume.Shocks to search values have a positive effect,which persisted for at least a week.Our findings contribute to the debate on cryptocurrencies/Bitcoins and have profound implications in terms of understanding their dynamics,which are of special interest to investors and economic policymakers.
文摘The valuation of cryptocurrencies is important given the increasing significance of this potential asset class.However,most state-of-the-art cryptocurrency valuation methods only focus on one of the fundamental factors or sentiments and use out-of-date data sources.In this study,a robust cryptocurrency valuation method is developed using up-to-date datasets.Using various panel regression models and moving-window regression tests,the impacts of fundamental factors and sentiments in the valuation of cryptocurrencies are explored with data covering from January 1,2009 to April 30,2023.The research shows the importance of sentiments and suggests that the fear and greed index can indicate when to make a cryptocurrency investment,while Google search interest in cryptocurrency is crucial when choosing the appropriate type of cryptocurrency.Moreover,consensus mechanism and initial coin offering have significant effects on cryptocurrencies without stablecoins,while their impacts on cryptocurrencies with stablecoins are insignificant.Other fundamental factors,such as the type of supply and the presence of smart contracts,do not have a significant influence on cryptocurrency.Findings from this study can enhance cryptocurrency marketisation and provide insightful guidance for investors,portfolio managers,and policymakers in assessing the utility level of each cryptocurrency.
