After decades of advance development, China's stock market has gradually arisen into one of the world's most important capital markets. The stock price index can well reflect the health status and macro change trend of a country's economic development, which can be said to be a barometer of the country's economic development. Studying the stock price index forecast is of great significance to the entire national economy and to each investor. Using 2 tools, Python and EViews8.0, and taking the Shanghai Stock Exchange 50 index as an example, the long short-term memory (LSTM) model in deep learning (DL) and the Autoregressive Integrated Moving Average (ARIMA) model are selected for fitting and prediction. The research results explain that the Root Mean Squared Error (RMSE) of LSTM model is lower, and the model based on DL method has stronger prediction ability on stock price index than traditional stock prediction model. This model is an effective stock prediction method.
BackgroundThe COVID-19 pandemic has brought new challenges and attention to the mental health of all social groups, making mental health increasingly necessary and important. However, people only focus on the mental health of undergraduates, and the mental health of teachers has not received much attention from society. College teachers are the backbone of the teachers' group, and their mental health not only affects the teaching quality and research level but also plays an important role in the mental health and personality development of undergraduates.MethodDuring the COVID-19 pandemic, online teaching is a major challenge for college teachers, especially English teachers. To this end, this article proposes a bipartite graph convolutional network (BGCN) model based on the psychological test questionnaire and its structural characteristics for the recognition of the mental health crisis.ResultsExperimental results show that the proposed BGCN model is superior to neural network algorithms and other machine learning algorithms in accuracy, precision, F1, and recall and can be well used for the mental health management of English teachers in the era of COVID-19.
The representation of time in sentences is the key problem for tense collocation. Based on the relationship expression among regions in Allen’s interval algebra theory, we propose a vector representation method, i.e., relationship vector, and several operations are defined based on the relationship vector for temporal reasoning in this work. This method transforms the original matrix representation into vector representation, which reduces the amount of computation of temporal reasoning. In addition, we propose a temporal classification and collocation method based on deep learning and deep reinforcement learning. This method uses a bidirectional cyclic neural network and a convolutional neural network for text expression and achieves temporal word classification and temporal collocation based on the deep reinforcement learning model. In the experiments, the proposed method obtains the average accuracy of 92.17% in five datasets, i.e., MPQA, CR, MR, Subj, and TREC, which proves its effectiveness in tense collocation.
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