Stock market has a profound impact on the market economy,Hence,the prediction of future movement of stocks is of great significance to investors.Therefore,an efficient prediction system can solve this problem to a gre...Stock market has a profound impact on the market economy,Hence,the prediction of future movement of stocks is of great significance to investors.Therefore,an efficient prediction system can solve this problem to a great extent.In this paper,we used the stock price of Google Inc.as a prediction object,selected 3810 adjusted closing prices,and used long short-term memory(LSTM)method to predict the future price trend of the stock.We built a three-layer LSTM model and divided the entire data into a test set and a training set according to the ratio of 8 to 2.The final results show that while the LSTM model can predict the stock trend of Google Inc.very well,it cannot predict the specific price accurately.展开更多
As the scale of urban rail transit(URT)networks expands,the study of URT resilience is essential for safe and efficient operations.This paper presents a comprehensive review of URT resilience and highlights potential ...As the scale of urban rail transit(URT)networks expands,the study of URT resilience is essential for safe and efficient operations.This paper presents a comprehensive review of URT resilience and highlights potential trends and directions for future research.First,URT resilience is defined by three primary abilities:absorption,resistance,and recovery,and four properties:robustness,vulnerability,rapidity,and redundancy.Then,the metrics and assessment approaches for URT resilience were summarized.The metrics are divided into three categories:topology-based,characteristic-based,and performance-based,and the assessment methods are divided into four categories:topological,simulation,optimization,and datadriven.Comparisons of various metrics and assessment approaches revealed that the current research trend in URT resilience is increasingly favoring the integration of traditional methods,such as conventional complex network analysis and operations optimization theory,with new techniques like big data and intelligent computing technology,to accurately assess URT resilience.Finally,five potential trends and directions for future research were identified:analyzing resilience based on multisource data,optimizing train diagram in multiple scenarios,accurate response to passenger demand through new technologies,coupling and optimizing passenger and traffic flows,and optimal line design.展开更多
文摘Stock market has a profound impact on the market economy,Hence,the prediction of future movement of stocks is of great significance to investors.Therefore,an efficient prediction system can solve this problem to a great extent.In this paper,we used the stock price of Google Inc.as a prediction object,selected 3810 adjusted closing prices,and used long short-term memory(LSTM)method to predict the future price trend of the stock.We built a three-layer LSTM model and divided the entire data into a test set and a training set according to the ratio of 8 to 2.The final results show that while the LSTM model can predict the stock trend of Google Inc.very well,it cannot predict the specific price accurately.
基金supported by the National Natural Science Foundation of China(72288101,72331001,and 72071015)the Research Grants Council of the Hong Kong Special Administrative Region(PolyU 15222221)+1 种基金the 111 Center(B20071)an XPLORER PRIZE.
文摘As the scale of urban rail transit(URT)networks expands,the study of URT resilience is essential for safe and efficient operations.This paper presents a comprehensive review of URT resilience and highlights potential trends and directions for future research.First,URT resilience is defined by three primary abilities:absorption,resistance,and recovery,and four properties:robustness,vulnerability,rapidity,and redundancy.Then,the metrics and assessment approaches for URT resilience were summarized.The metrics are divided into three categories:topology-based,characteristic-based,and performance-based,and the assessment methods are divided into four categories:topological,simulation,optimization,and datadriven.Comparisons of various metrics and assessment approaches revealed that the current research trend in URT resilience is increasingly favoring the integration of traditional methods,such as conventional complex network analysis and operations optimization theory,with new techniques like big data and intelligent computing technology,to accurately assess URT resilience.Finally,five potential trends and directions for future research were identified:analyzing resilience based on multisource data,optimizing train diagram in multiple scenarios,accurate response to passenger demand through new technologies,coupling and optimizing passenger and traffic flows,and optimal line design.
