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Based on a literature review of influencing factors and forecasting methods for grain production, the empirical analysis of the influencing factors of China's grain output is performed using the full subset regression method, the ridge regression method, and the LASSO regression method. The results show that (1) the increase in the sown area of grain crops is the main reason for the increase in grain output, (2) the use of agricultural fertilizers and the increase in rural electricity consumption are the driving factors for the increase in grain output, (3) the impact of total power of agricultural machinery is limited, and (4) natural disasters have a certain negative impact on food production. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]