Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.
With the changes in the population structure, China's population policy has been continuously adjusted since the 1980s. In early 2016, China began to implement the universal two-child policy, hoping to cope with the aging population, the declining birthrate, and the decline in labor supply. However, it was not satisfactory. Based on demographic data, this paper analyzes the background of the universal two-child policy, as well as the current changes in the labor supply situation and existing problems in China, and put forward suggestions to further improve the birth policy, increase the willingness of women of childbearing age to give birth, improve the quality of employment, and strengthen human capital investment.
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