Existing research has shown that political crisis events can directly impact the tourism industry.However,the current methods suffer from potential changes of unobserved variables,which poses challenges for a reliable...Existing research has shown that political crisis events can directly impact the tourism industry.However,the current methods suffer from potential changes of unobserved variables,which poses challenges for a reliable evaluation of the political crisis impacts.This paper proposes a panel counterfactual approach with Internet search index,which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest.It also provides a tool to examine potential channels through which the crisis may affect tourist outflows.This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea.Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place.This paper provides essential evidence to help decision-makers improve the management of the tourism crisis.展开更多
Coronavirus disease 2019(COVID-19)is an emerging infectious disease,and it is important to detect early and monitor the disease trend for policymakers to make informed decisions.We explored the predictive utility of B...Coronavirus disease 2019(COVID-19)is an emerging infectious disease,and it is important to detect early and monitor the disease trend for policymakers to make informed decisions.We explored the predictive utility of Baidu Search Index and Baidu Information Index for early warning of COVID-19 and identified search keywords for further monitoring of epidemic trends in Guangxi.A time-series analysis and Spearman correlation between the daily number of cases and both the Baidu Search Index and Baidu Information Index were performed for seven keywords related to COVID-19 from January 8 to March 9,2020.The time series showed that the temporal distributions of the search terms“coronavirus,”“pneumonia”and“mask”in the Baidu Search Index were consistent and had 2 to 3 days'lead time to the reported cases;the correlation coefficients were higher than 0.81.The Baidu Search Index volume in 14 prefectures of Guangxi was closely related with the number of reported cases;it was not associated with the local GDP.The Baidu Information Index search terms“coronavirus”and“pneumonia”were used as frequently as 192,405.0 and 110,488.6 per million population,respectively,and they were also significantly associated with the number of reported cases(rs>0.6),but they fluctuated more than for the Baidu Search Index and had 0 to 14 days'lag time to the reported cases.The Baidu Search Index with search terms“coronavirus,”“pneumonia”and“mask”can be used for early warning and monitoring of the epidemic trend of COVID-19 in Guangxi,with 2 to 3 days'lead time.展开更多
In this paper, we propose a new method based on index to realize IR-style Chinese keyword search with ranking strategies in relational databases. This method creates an index by using the related information of tuple ...In this paper, we propose a new method based on index to realize IR-style Chinese keyword search with ranking strategies in relational databases. This method creates an index by using the related information of tuple words and presents a ranking strategy in terms of the nature of Chinese words. For a Chinese keyword query, the index is used to match query search words and the tuple words in index quickly, and to compute similarities between the query and tuples by the ranking strategy, and then the set of identifiers of candidate tuples is generated. Thus, we retrieve top-N results of the query using SQL selection statements and output the ranked answers according to the similarities. The experimental results show that our method is efficient and effective.展开更多
Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index predict...Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index prediction. The parameters (C, σ) of SVR models were selected by three different methods of grid search (GRID), particle swarm optimization (PSO) and genetic algorithm (GA).The optimized parameters were used to predict the opening price of the test samples. The predictive results shown that the SVR model with GRID (GRID-SVR), the SVR model with PSO (PSO-SVR) and the SVR model with GA (GA-SVR) were capable to fully demonstrate the time-dependent trend of stock index and had the significant prediction accuracy. The minimum root mean square error (RMSE) of the GA-SVR model was 15.630, the minimum mean absolute percentage error (MAPE) equaled to 0.39% and the correspondent optimal parameters (C, σ) were identified as (45.422, 0.012). The appreciated modeling results provided theoretical and technical reference for investors to make a better trading strategy.展开更多
针对传统近邻传播聚类算法以数据点对之间的相似度作为输入度量,由于需要预设偏向参数p和阻尼系数λ,算法精度无法精确控制的问题,提出了一种跳跃跟踪麻雀搜索算法优化的交叉迭代近邻传播聚类方法.首先,针对麻雀搜索算法中发现者和加入...针对传统近邻传播聚类算法以数据点对之间的相似度作为输入度量,由于需要预设偏向参数p和阻尼系数λ,算法精度无法精确控制的问题,提出了一种跳跃跟踪麻雀搜索算法优化的交叉迭代近邻传播聚类方法.首先,针对麻雀搜索算法中发现者和加入者位置更新不足的问题,设计了一种跳跃跟踪优化策略,通过考虑偏好阻尼因子的跳跃策略设计大步长更新发现者,增加麻雀搜索算法的全局勘探能力和寻优速度,加入者设计动态小步长跟踪领头雀更新位置,同时,利用自适应种群划分机制更新发现者和加入者的比重,增加算法的后期局部开发能力和寻优速度;其次,设计基于扰动因子的Tent映射,在此基础上增加3个参数,使映射分布范围增大,并避免了陷入小周期点和不稳周期点;最后,引入轮廓系数作为评价函数,跳跃跟踪麻雀搜索算法自动寻找较优的p和λ,代替手动输入参数,并融合基于扰动因子的Tent映射优化近邻传播算法,交叉迭代确定最优簇数.使用多种算法聚类University of California Irvine数据集的10种公共数据集,仿真结果表明,本文提出的聚类算法与经典近邻传播算法、基于差分改进的仿射传播聚类算法、基于麻雀搜索算法优化的近邻传播聚类算法和进化近邻传播算法相比具有更优的搜索效率以及聚类精度.对国家信息数据进行了聚类分析,提出的方法更加准确有效合理,具有较好的应用价值.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.72203246(HUANG Bai's work)the National Natural Science Foundation of China under Grant Nos.72322016,72073126,71988101,71973116 and 72091212Young Elite Scientists Sponsorship Program by CAST (SUN Yuying's work)。
文摘Existing research has shown that political crisis events can directly impact the tourism industry.However,the current methods suffer from potential changes of unobserved variables,which poses challenges for a reliable evaluation of the political crisis impacts.This paper proposes a panel counterfactual approach with Internet search index,which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest.It also provides a tool to examine potential channels through which the crisis may affect tourist outflows.This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea.Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place.This paper provides essential evidence to help decision-makers improve the management of the tourism crisis.
基金supported by the Health and Emergency Skills Training Center of Guangxi(HESTCG202104)National Natural Science Foundation of China(11971479)Guangxi Bagui Honor Scholarship and Chinese State Key Laboratory of Infectious Disease Prevention and Control.
文摘Coronavirus disease 2019(COVID-19)is an emerging infectious disease,and it is important to detect early and monitor the disease trend for policymakers to make informed decisions.We explored the predictive utility of Baidu Search Index and Baidu Information Index for early warning of COVID-19 and identified search keywords for further monitoring of epidemic trends in Guangxi.A time-series analysis and Spearman correlation between the daily number of cases and both the Baidu Search Index and Baidu Information Index were performed for seven keywords related to COVID-19 from January 8 to March 9,2020.The time series showed that the temporal distributions of the search terms“coronavirus,”“pneumonia”and“mask”in the Baidu Search Index were consistent and had 2 to 3 days'lead time to the reported cases;the correlation coefficients were higher than 0.81.The Baidu Search Index volume in 14 prefectures of Guangxi was closely related with the number of reported cases;it was not associated with the local GDP.The Baidu Information Index search terms“coronavirus”and“pneumonia”were used as frequently as 192,405.0 and 110,488.6 per million population,respectively,and they were also significantly associated with the number of reported cases(rs>0.6),but they fluctuated more than for the Baidu Search Index and had 0 to 14 days'lag time to the reported cases.The Baidu Search Index with search terms“coronavirus,”“pneumonia”and“mask”can be used for early warning and monitoring of the epidemic trend of COVID-19 in Guangxi,with 2 to 3 days'lead time.
文摘In this paper, we propose a new method based on index to realize IR-style Chinese keyword search with ranking strategies in relational databases. This method creates an index by using the related information of tuple words and presents a ranking strategy in terms of the nature of Chinese words. For a Chinese keyword query, the index is used to match query search words and the tuple words in index quickly, and to compute similarities between the query and tuples by the ranking strategy, and then the set of identifiers of candidate tuples is generated. Thus, we retrieve top-N results of the query using SQL selection statements and output the ranked answers according to the similarities. The experimental results show that our method is efficient and effective.
文摘Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index prediction. The parameters (C, σ) of SVR models were selected by three different methods of grid search (GRID), particle swarm optimization (PSO) and genetic algorithm (GA).The optimized parameters were used to predict the opening price of the test samples. The predictive results shown that the SVR model with GRID (GRID-SVR), the SVR model with PSO (PSO-SVR) and the SVR model with GA (GA-SVR) were capable to fully demonstrate the time-dependent trend of stock index and had the significant prediction accuracy. The minimum root mean square error (RMSE) of the GA-SVR model was 15.630, the minimum mean absolute percentage error (MAPE) equaled to 0.39% and the correspondent optimal parameters (C, σ) were identified as (45.422, 0.012). The appreciated modeling results provided theoretical and technical reference for investors to make a better trading strategy.
文摘针对传统近邻传播聚类算法以数据点对之间的相似度作为输入度量,由于需要预设偏向参数p和阻尼系数λ,算法精度无法精确控制的问题,提出了一种跳跃跟踪麻雀搜索算法优化的交叉迭代近邻传播聚类方法.首先,针对麻雀搜索算法中发现者和加入者位置更新不足的问题,设计了一种跳跃跟踪优化策略,通过考虑偏好阻尼因子的跳跃策略设计大步长更新发现者,增加麻雀搜索算法的全局勘探能力和寻优速度,加入者设计动态小步长跟踪领头雀更新位置,同时,利用自适应种群划分机制更新发现者和加入者的比重,增加算法的后期局部开发能力和寻优速度;其次,设计基于扰动因子的Tent映射,在此基础上增加3个参数,使映射分布范围增大,并避免了陷入小周期点和不稳周期点;最后,引入轮廓系数作为评价函数,跳跃跟踪麻雀搜索算法自动寻找较优的p和λ,代替手动输入参数,并融合基于扰动因子的Tent映射优化近邻传播算法,交叉迭代确定最优簇数.使用多种算法聚类University of California Irvine数据集的10种公共数据集,仿真结果表明,本文提出的聚类算法与经典近邻传播算法、基于差分改进的仿射传播聚类算法、基于麻雀搜索算法优化的近邻传播聚类算法和进化近邻传播算法相比具有更优的搜索效率以及聚类精度.对国家信息数据进行了聚类分析,提出的方法更加准确有效合理,具有较好的应用价值.
