The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the ...The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets.By examining historical transaction data,latent opportunities for profit can be uncovered,providing valuable insights for both institutional and individual investors to make more informed decisions.This study focuses on analyzing historical transaction data from four banks to predict closing price trends.Various models,including decision trees,random forests,and Long Short-Term Memory(LSTM)networks,are employed to forecast stock price movements.Historical stock transaction data serves as the input for training these models,which are then used to predict upward or downward stock price trends.The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements.The LSTM-based deep neural network model,in particular,demonstrates a commendable level of predictive accuracy.This conclusion is reached following a thorough evaluation of model performance,highlighting the potential of LSTM models in stock market forecasting.The findings offer significant implications for advancing financial forecasting approaches,thereby improving the decision-making capabilities of investors and financial institutions.展开更多
脱硫过程是具有高度动态非线性和较大延迟时间的复杂工业过程,为了解决烟气脱硫过程的建模问题,设计了注意力机制下的深度长短期记忆(attention mechanism-based long short-term memory,AttLSTM)网络,并基于该网络设计自动编码器,完成...脱硫过程是具有高度动态非线性和较大延迟时间的复杂工业过程,为了解决烟气脱硫过程的建模问题,设计了注意力机制下的深度长短期记忆(attention mechanism-based long short-term memory,AttLSTM)网络,并基于该网络设计自动编码器,完成脱硫过程异常点的检测。该文首次提出使用AttLSTM网络自编码器对脱硫过程进行离群点检测,并且该网络模型同样首次应用于脱硫过程的辨识任务中。从更深的意义上讲,该文尝试使用深度学习模型对复杂系统进行辨识,所建立的AttLSTM网络之前未出现在系统辨识领域,该网络的出现可以丰富辨识模型的选择,同时为人工智能技术在系统辨识领域和控制领域的应用与推广提供参考。实验结果表明,相比于之前文献出现的脱硫过程建模方法,所提方法在不同性能指标上均具有更好的表现,由此可以证明深度AttLSTM网络在脱硫场景下的有效性。展开更多
文摘The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets.By examining historical transaction data,latent opportunities for profit can be uncovered,providing valuable insights for both institutional and individual investors to make more informed decisions.This study focuses on analyzing historical transaction data from four banks to predict closing price trends.Various models,including decision trees,random forests,and Long Short-Term Memory(LSTM)networks,are employed to forecast stock price movements.Historical stock transaction data serves as the input for training these models,which are then used to predict upward or downward stock price trends.The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements.The LSTM-based deep neural network model,in particular,demonstrates a commendable level of predictive accuracy.This conclusion is reached following a thorough evaluation of model performance,highlighting the potential of LSTM models in stock market forecasting.The findings offer significant implications for advancing financial forecasting approaches,thereby improving the decision-making capabilities of investors and financial institutions.
文摘脱硫过程是具有高度动态非线性和较大延迟时间的复杂工业过程,为了解决烟气脱硫过程的建模问题,设计了注意力机制下的深度长短期记忆(attention mechanism-based long short-term memory,AttLSTM)网络,并基于该网络设计自动编码器,完成脱硫过程异常点的检测。该文首次提出使用AttLSTM网络自编码器对脱硫过程进行离群点检测,并且该网络模型同样首次应用于脱硫过程的辨识任务中。从更深的意义上讲,该文尝试使用深度学习模型对复杂系统进行辨识,所建立的AttLSTM网络之前未出现在系统辨识领域,该网络的出现可以丰富辨识模型的选择,同时为人工智能技术在系统辨识领域和控制领域的应用与推广提供参考。实验结果表明,相比于之前文献出现的脱硫过程建模方法,所提方法在不同性能指标上均具有更好的表现,由此可以证明深度AttLSTM网络在脱硫场景下的有效性。
