Abstract
Abstract. The development of the stock market has been characterised by a lack of dynamism, particularly in the context of an economic slowdown, policy adjustments and global uncertainties. As a result, corporate earnings have declined, stock demand has decreased, stock prices have fallen and investors are uncertain about the future. The following article aims to demonstrate the construction of an autoregressive integrated moving average (ARIMA) model using the Python programming language, the utilization of the CSI 300 index data from 2012 to 2022 sourced from the Kaggle website, the prediction of its fluctuations, the generation of a line graph, and a comparison with the actual trend. In the final stage of the analysis, four diagnostic graphs will be deployed to ascertain the suitability of the model. The results show that the ARIMA model effectively forecasts stock trends, with the predicted upward and downward trends following 2022 largely aligning with the actual trends and overlapping the two lines. Investors can use this model to determine the best investment direction and reduce the risk of failure.
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